Systems and methods for automatically recording and providing video at a ski resort

ABSTRACT

A system for monitoring and recording and processing an activity includes one or more cameras for automatically recording video of the activity. A processor and memory associated and in communication with the camera is disposed near the location of the activity. The system may include AI logic configured to identify a user recorded within a video frame captured by the camera. The system may also detect and identify a user when the user is located within a predetermined area. The system may include a video processing engine configured to process images within the video frame to identify the user and may modify and format the video upon identifying the user and the activity. The system may include a communication module to communicate formatted video to a remote video processing system, which may further process the video and enable access to a mobile app of the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/886,619, filed Aug. 12, 2022, which is a continuation of U.S. patentapplication Ser. No. 17/680,788, filed Feb. 25, 2022, now U.S. Pat. No.11,551,451, which is a continuation of U.S. patent application Ser. No.17/234,284, filed Apr. 19, 2021, now U.S. Pat. No. 11,270,125, which isa continuation of U.S. patent application Ser. No. 15/931,484, filed May13, 2020, now U.S. Pat. No. 11,003,914, which claims the benefit ofpreviously filed U.S. Provisional Patent Application No. 62/847,052,filed May 13, 2019, the entire contents of which are hereby incorporatedby reference in their entirety.

FIELD OF THE INVENTION

The present disclosure is directed to video recordings of activities.More particularly, the present disclosure is directed to automaticallymonitoring and recording athletic activities, such as golf, using videocameras and computer processing.

BACKGROUND

Amateur and professional athletes participate in a wide range ofathletic events and competitions. In the case of professional athletics,it is typical for the athletic competition to be recorded and broadcaston television and/or over the internet. Broadcasters often work togetherwith the athletes or a governing body of the athletic event to provideaccess to the event and to provide the equipment necessary to record andbroadcast the event, including multiple cameras and microphonesdispersed at fixed locations or at mobile locations. These arrangementsare typical in most professional spectator sports, such as golf, tennis,football, baseball, basketball, soccer, hockey, auto racing, cycling,etc. These arrangements are also typical for collegiate athletics, withevents that are often covered with the same or similar types of accessand cameras.

In the case of other athletic events, such as typical recreationalactivities undertaken by amateur non-collegiate athletes, videorecording and/or broadcast is not typical, due to the lack of widespreadpublic demand to observe recreational golf, tennis, basketball, or thelike. However, it is desirable in many cases by the athletesparticipating to have their activity recorded, similar to the recordingsprovided in professional events. Such recordings can be viewed at alater time for entertainment or evaluation purposes. For example, agolfer may desire to record his swing to be able to analyze his swingmechanics for potential improvements. Similarly, a golfer may want torecord his shots. Basketball players may similarly want to record theirshooting motion and resulting shot, or their movement on the court forevaluating the effectiveness of particular offensive plays or defensivepositioning.

However, it is necessary for the recreational athlete to make their ownarrangements for their events to be recorded. The athlete may need toprovide and set up their own recording equipment and set up their ownrecording equipment in an effective manner to ensure that the event issufficiently recorded. Alternatively, the athlete may need to secure theservices of a 3^(rd) party who can handle recording equipment to obtaindifferent views or adjust to player movement.

In some instances, athletes may provide themselves with a body-mountedcamera to record a particular aspect of an event. For example, ahead-mounted camera may be used to record a particular cycling route. Arock climber may similarly use a body-mounted camera to record aparticular climbing route. These body-mounted cameras are limited,however, in that they typically cannot record the body movements of anathlete in particular. Moreover, the head mounted camera is typicallydirected according to the position of the user's head, and therefore mayonly record a limited area corresponding to what the athlete is lookingat. In many cases, the camera will not even record what the athlete isfocusing on because the camera does not adjust in response to eyemovement or changing focus.

Some athletic locations may have recording equipment pre-installed, suchas at a basketball court or indoor tennis court, where the playingsurface and area are known and constant. However, this is typically notpossible for many outdoor recreational activities because of the myriadlocations where the athlete may be situated. For example, on a golfcourse, it is difficult to predict with certainty the path of a golfball or the location of the golfer on the course, because each locationis dependent on the previous location and ball flight from that previouslocation. For runners, cyclists, climbers, and the like, the locationmay be even more unpredictable. Accordingly, for recording these typesof activities, it is typically necessary for the recreational athlete tohave another individual following them and recording them to provide ausable recording.

In view of the above, improvements can be made to recording systems forathletic events.

SUMMARY

It is an aspect of the disclosure to provide a system for automaticallyrecording an athletic event.

It is yet another aspect of the disclosure to provide a system forautomatically processing a recording of an athletic event and displayingthe recording with additional information.

It is yet another aspect of the disclosure to provide a system forautomatically recording a golf shot and processing the images of thegolf shot.

In view of these and other aspects, a system for automatically recordingand processing an activity is provided. The system includes a firstremote camera disposed at a first geographic location to record a videoof a first predetermined activity. The system further includes a firstprocessor and memory operatively associated with the first remote cameraand in communication with the camera. The first processor and memory arelocated near the first geographic location. The system further includesa local video processing engine associated with the first processor andmemory. The local video processing engine is configured to processframes of video captured by the first remote camera of a first userparticipating in the first predetermined activity. The first processoris further configured to modify the video upon identifying the firstuser and the first predetermined activity. The system further includes acommunication module capable of communicating a formatted video to aremote video processing system. The remote video processing system isconfigured to further process the formatted video and enable access ofthe processed video to the first user.

In one aspect, the processor is configured to detect a position of thefirst user using one or more of: a geofence; a GPS location; facialrecognition; object recognition; clothing recognition; motion detection;RFID detection; Bluetooth detection; Wi-Fi detection; Radar sensing; andHeat sensing.

In one aspect, the system includes an artificial intelligent (AI) logicaccessible to the first processor and configured with logic to identifyone or more users recorded within a video frame captured by the firstremote camera.

In one aspect, the first processor is configured to: identify the firstuser; automatically record the first user when the first user ispositioned within a first predetermined area; associate a firstrecording of the first user with the first user; associate a secondrecording of the first user with the first user; and process the firstand second recording of the first user and to define the formatted videoassociated with the first user.

In one aspect, the first processor is configured to detect and identifya second user within the first predetermined area.

In one aspect, the first processor is configured to automatically recordthe second user and the first processor is configured to process arecording associated with the second user.

In one aspect, the first processor is configured to determine which userof the first and second user is to be recorded.

In one aspect, the first processor is configured to transmit a messageto the first user or the second user indicating that the first user orthe second user will be recorded.

In one aspect, the first processor is configured to generate a locationgrid over at least a portion of the first geographic location.

In one aspect, the first processor is configured to detect a location ofthe first user within the location grid and further configured to detecta location of an object associated with the first user within thelocation grid.

In one aspect, the first processor is configured to monitor a locationof the first user while the first user is located within the firstgeographic location.

In one aspect, the first processor is configured to add a graphicalelement to the first processed recording based on data associated withthe recording.

In one aspect, the graphical element can include one or more graphicalelements added to one or more frames of the video, the graphicalelements including: textual information displaying a location of thevideo; one or more names of the location where the video was recorded;the name of the first user; the date the video was recorded; a logo orother marketing images associated with the location; a colored tracecreated to show a graphic line between frames of a moving object;graphic objects; and augmented reality elements.

In one aspect, the colored trace includes creating a trace for one ormore of: a golf ball flight path; a football player running path; afootball flight path; a soccer player running path; a soccer ball flightpath; a downhill skier or snowboarder path; a swimming path of aswimmer; a swimming path of a fish caught by the first user; a boatingpath of a catamaran or boat racing vessel; and a biking path of amountain or race bike.

In another aspect, a system for automatically recording and processingan activity is provided. The system includes a first remote cameradisposed at a first geographic location to record a video of a firstpredetermined activity. The system further includes a first processorand memory operatively associated with the first remote camera and incommunication with the camera. The first processor and memory arelocated near the first geographic location. The system further includesan artificial intelligent (AI) logic accessible to the processor andconfigured with logic to identify a user recorded within a video framecaptured by the first remote camera. The system further includes a localvideo processing engine associated with the first processor and memory.The local video processing engine is configured to process images withinthe video frame to identify the first user. The first processor isfurther configured to modify the video upon identifying the first userand the first predetermined activity. The system further includes acommunication module capable of communicating a formatted video to aremote video processing system. The remote video processing system isconfigured to further process the formatted video and enable access to amobile app of the identified first user.

In one aspect, the AI logic includes logic capable of identifying one ormore of: a golfer; a golf ball; a shirt; a shirt color; pants; a pantscolor; a skirt; a skirt color; a hat; a hat color; a golf glove; golfshoes; a golf cart; one or more persons in a golf cart; a golf tee; agolf club; an iron; a driver; a utility club; a putter; a wedge; a golfball logo; a male; a female; a child; a junior; a shirt logo; a caddie;a marshal; a brand; a left handed golfer; a right handed golfer; avisor; glasses; sunglasses; a beverage; a tee box; a color of a tee box;trees; a fairway; a cart path; a green; a pin; a hole; a sand bunker; awater hazard; a grass hazard; woods; out-of-bounds; rough; a first cutof a green; a second cut of a green; birds; bugs; animals; a distancefrom tee to pin; a distance from tee to front of green; a distance fromtee to center of green; a distance from tee to back of green; redstakes; white stakes; yellow stakes; change in elevation; clouds; rain;snow; fog; mist; mud; wind; topology of green; or cut of hole.

In one aspect, AI logic further comprises logic capable of identifyingan activity including one or more of: golf activity; football activity;soccer activity; lacrosse activity; baseball activity; basketballactivity; tennis activity; pickleball activity; beanbag toss activity;bowling activity; billiards activity; swimming activity; divingactivity; racing activity; hockey activity; field hockey activity; discgolf activity; rugby activity; skiing activity; snowboarding activity;biking activity; fishing activity; boating activity; and sportsactivity.

In one aspect, the remote video processing system further includes: aremote video processing and management system in communication with thefirst processor, the remote video processing and management systemconfigured to receive a first series of recorded videos and process thefirst series of recorded videos to create the AI logic. The processingof the first series of recorded videos includes tagging one or moreuniquely identified objects within one or more frames of each of therecorded videos, the tagging including tags for identifying users anduser activities within the recorded video. The processing furtherincludes creating the AI logic including a neural network of taggedobjects and distributing the AI logic to the first processor for use inprocessing video at the predetermined location. The processing furtherincludes receiving the formatted video from the communication module andmodifying the formatted video based on use of the mobile app.

In one aspect, the system includes a remote video processing andmanagement system in communication with the video processing system. Theremote video processing management system includes: a network processorcoupled to cloud storage storing processed video received from the videoprocessing system and the first remote camera; an artificialintelligence (AI) enabled graphics processing engine configured toaccess the formatted video and process the formatted video; graphicalassets accessible to the AI enabled graphics processing engine to beadded to one or more frames of the formatted video; a format managerconfigured to format the formatted video based on a distributionlocation of the formatted video; a distribution manager accessible tothe network processor, the distribution manager configured to distributethe formatted video to the distribution location.

In one aspect, the system includes a mobile app associated with thedistribution manager, the mobile app including content provided by thedistribution manager including: one or more formatted videos output bythe remote video processing and management system; a list of videoscreated using one or more activities; a list of location where the oneor more videos were recorded; a social media feature to share theformatted video; and a virtual coaching feature configured to enable acoach to access and comment on the formatted video of the first user.

In one aspect, the processor is configured to identify a second userrecorded within the video frame captured by the first remote camera. Theprocessor is configured to extract video frames from the first video,the video frames including the second user. The processor is configuredto combine the extracted video frames into a second formatted video, thesecond formatted video including the second user. The communicationmodule communicates the second formatted video to the remote videoprocessing system configured to further process the video and enableaccess to a mobile app of the identified second user.

In one aspect, the remote video processing and management system isfurther configured to: identify the second user in proximity to thefirst user during the recorded activity; initiate access of a secondvideo generated by the remote video processing system to the first user;and enable the first user access to the second video using the mobileapp.

In another aspect, a method of automatically recording and providingvideo is provided. The method includes recording a video of apredetermined activity using a first remote camera located at a firstgeographic location. The method further includes processing the video atthe first geographic location. The processing includes: identifying afirst user performing the predetermined activity; extracting imageframes from the video including the first user during the predeterminedactivity; and merging the extracted image frames to generate a formattedvideo. The method includes outputting the formatted video to a remotevideo processing system for additional processing.

In one aspect, the method further includes: identifying a second userperforming the predetermined activity within the video; extractingadditional image frames including the second user performing thepredetermined activity; merging the additional image frames to generatea second formatted video; and outputting the second formatted video tothe remote video processing system.

In one aspect, the method includes: providing a second remote camera atthe first geographic location; identifying the first geographic locationas a golf hole on a golf course; establishing a first geofence aroundthe tee box of the golf hole; establishing a second geofence around thegreen of the golf hole; detecting when a first user is within the firstgeofence; activating recording of the first remote camera and the secondremote camera in response to identifying the first user within the firstgeofence to record the predetermined activity; detecting when the firstuser is within the second geofence; detecting when the first user leavesthe second geofence; and disabling recording of the predeterminedactivity when the first user leaves the second geofence.

In one aspect, the first geofence includes a first geofence radius andthe second geofence includes a second geofence radius that is differentthan the first geofence radius.

In one aspect, the first geofence includes a first geofence size and thesecond geofence includes a second geofence size that is different thanthe first geofence size.

In one aspect, the method includes: identifying the predeterminedactivity as a golf activity; extracting a first image frame to identifya golf ball at a first location within the first image frame; extractinga second image frame at a period of time later than the first imageframe; determining if the golf ball moved to another location within thesecond image frame; drawing a colored line from within the second imageframe that extends to the first location; repeating drawing the linewith subsequent frames until the golf ball is no longer visible;estimating where the golf ball lands; and drawing a colored line to theestimated location of where the golf ball lands.

In one aspect, the method further includes: creating an AI logic usingpreviously recorded activities to identify a specific activity;identifying the specific activity using the AI logic; identifying thefirst user performing the specific activity; initiating extracting ofimage frames of the specific activity; and combining the extracted imageframes of the specific activity.

In one aspect, the creating an AI logic further comprises the steps of:identifying a golfer holding a golf club within a previously recordedvideo; tagging the golfer having specific clothes, a golf club, and agolf ball; repeating the identifying and tagging steps over numerouspreviously recorded activities and image frames; generating the AI logicusing the tagged images; and using the AI logic at a golf course havingthe first remote camera.

In another aspect, a method of automatically recording an athleticperformance is provided. The method includes: detecting, by a processor,that at least one player is positioned within a predetermined area;identifying a first player of the at least one player; automaticallyrecording, by at least one camera operatively coupled to the processor,a performance of the first player and defining a first recording;automatically storing, in a database operatively coupled to theprocessor, the first recording; automatically correlating the firstrecording with the first player; and automatically processing the firstrecording and defining a first processed recording.

In one aspect, the at least one camera comprises a first camera and asecond camera.

In another aspect, the method includes identifying, at the processor, afirst geographic position of the first camera and a second geographicposition of the second camera.

In another aspect, the method includes identifying the predeterminedarea at the processor.

In another aspect the method includes defining a location grid withinthe predetermined area.

In another aspect, the method includes detecting an object associatedwith the first player at a grid location within the location grid.

In another aspect, the method includes, in response to detecting theobject at the grid location, automatically adding a graphic associatedwith the location to the first processed recording.

In another aspect, an identity of the first player is determined viaimage recognition.

In another aspect, the method includes communicating, by the processorto a mobile device associated with the first player, a signalinstructing the player to perform.

In another aspect, the method includes automatically transmitting thefirst processed recording to the first user.

BRIEF DESCRIPTION OF THE DRAWINGS

Other aspects of the present disclosure will be readily appreciated, asthe same becomes better understood by reference to the followingdetailed description when considered in connection with the accompanyingdrawings wherein:

FIG. 1 is a block diagram illustrating a video processing system fordetecting and recording activities in accordance with an aspect of thepresent disclosure;

FIG. 2 is a block diagram illustrating an AI enabled camera for use witha video processing system in accordance with an aspect of the presentdisclosure;

FIG. 3A is a block diagram illustrating an AI enabled video processingsystem for local video processing in accordance with an aspect of thepresent disclosure;

FIG. 3B is a flow diagram of a method for local video processing inaccordance with an aspect of the present disclosure;

FIG. 4A is a block diagram illustrating an AI enabled video processingsystem for remote video processing in accordance with an aspect of thepresent disclosure;

FIG. 4B is a flow diagram of a method for processing video using AIenabled remote video processing in accordance with an aspect of thepresent disclosure;

FIG. 5 is a user interface illustrating a mobile device application inaccordance with an aspect of the present disclosure;

FIG. 6 is a block diagram illustrating one example of AI enabled videorecording system disposed at a golf course in accordance with an aspectof the present disclosure;

FIG. 7 illustrates a method of one aspect of the athletic monitoringsystem in accordance with an aspect of the present disclosure; and

FIG. 8 illustrates a method of one aspect of the athletic monitoringsystem in accordance with an aspect of the present disclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

The following description in combination with the Figures is provided toassist in understanding the teachings disclosed herein. The followingdiscussion will focus on specific implementations and embodiments of theteachings. This focus is provided to assist in describing the teachingsand should not be interpreted as a limitation on the scope orapplicability of the teachings. However, other teachings can certainlybe utilized in this application. The teachings can also be utilized inother applications and with several different types of architecturessuch as distributed computing architectures, client/serverarchitectures, or middleware server architectures and associatedcomponents.

Devices or programs that are in communication with one another need notbe in continuous communication with each other unless expresslyspecified otherwise. In addition, devices or programs that are incommunication with one another may communicate directly or indirectlythrough one or more intermediaries.

Embodiments discussed below describe, in part, distributed computingsolutions that manage all or part of a communicative interaction betweennetwork elements. In this context, a communicative interaction may beintending to send information, sending information, requestinginformation, receiving information, receiving a request for information,or any combination thereof. As such, a communicative interaction couldbe unidirectional, bidirectional, multi-directional, or any combinationthereof. In some circumstances, a communicative interaction could berelatively complex and involve two or more network elements. Forexample, a communicative interaction may be “a conversation” or seriesof related communications between a client and a server—each networkelement sending and receiving information to and from the other. Thecommunicative interaction between the network elements is notnecessarily limited to only one specific form. A network element may bea node, a piece of hardware, software, firmware, middleware, anothercomponent of a computing system, or any combination thereof.

For purposes of this disclosure, an athletic monitoring and recordingsystem can include any instrumentality or aggregate of instrumentalitiesoperable to compute, classify, process, transmit, receive, retrieve,originate, switch, store, display, manifest, detect, record, reproduce,handle, or utilize any form of information, intelligence, or data forbusiness, scientific, control, entertainment, or other purposes. Forexample, an athletic monitoring and recording system can be a personalcomputer, a PDA, a consumer electronic device, a smart phone, a cellularor mobile phone, a set-top box, a digital media subscriber module, acable modem, a fiber optic enabled communications device, a mediagateway, a home media management system, a network server or storagedevice, a switch router, wireless router, or other network communicationdevice, or any other suitable device and can vary in size, shape,performance, functionality, and price.

The system can include memory, one or more processing resources orcontrollers such as a central processing unit (CPU) or hardware orsoftware control logic. Additional components of the system can includeone or more storage devices, one or more wireless, wired or anycombination thereof of communications ports to communicate with externaldevices as well as various input and output (I/O) devices, such as akeyboard, a mouse, and a video display. The athletic monitoring andrecording system can also include one or more buses operable to transmitcommunications between the various hardware components.

In the description below, a flow charted technique or algorithm may bedescribed in a series of sequential actions. Unless expressly stated tothe contrary, the sequence of the actions and the party performing theactions may be freely changed without departing from the scope of theteachings. Actions may be added, deleted, or altered in several ways.Similarly, the actions may be re-ordered or looped. Further, althoughprocesses, methods, algorithms or the like may be described in asequential order, such processes, methods, algorithms, or anycombination thereof may be operable to be performed in alternativeorders. Further, some actions within a process, method, or algorithm maybe performed simultaneously during at least a point in time (e.g.,actions performed in parallel), can also be performed in whole, in part,or any combination thereof.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of features is notnecessarily limited only to those features but may include otherfeatures not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive-or and not to an exclusive-or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

Also, the use of “a” or “an” is employed to describe elements andcomponents described herein. This is done merely for convenience and togive a general sense of the scope of the invention. This descriptionshould be read to include one or at least one and the singular alsoincludes the plural, or vice versa, unless it is clear that it is meantotherwise. For example, when a single device is described herein, morethan one device may be used in place of a single device. Similarly,where more than one device is described herein, a single device may besubstituted for that one device.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Although methods and materialssimilar or equivalent to those described herein can be used in thepractice or testing of embodiments of the present invention, suitablemethods and materials are described below. All publications, patentapplications, patents, and other references mentioned herein areincorporated by reference in their entirety, unless a particular passageis cited. In case of conflict, the present specification, includingdefinitions, will control. In addition, the materials, methods, andexamples are illustrative only and not intended to be limiting.

To the extent not described herein, many details regarding specificmaterials, processing acts, and circuits are conventional and may befound in textbooks and other sources within the computing, electronics,and software arts.

Also used within the description are uses of Artificial Intelligence(AI) or AI Logic, Machine Learning, and Neural Networks. AI or AI Logicincludes a several categories of techniques that allow computers tomimic human capabilities. AI techniques or logic include MachineLearning, Speech and Language Processing, Expert Systems, and Robotics.Machine Learning is the subset of AI that enables computers to improveat tasks through experience. Machine Learning includes traditionalstatistics-based approaches such as Regression Analysis and newertechniques like Deep Learning. Deep Learning uses large amounts ofhistorical data to train multilevel Neural Networks to draw conclusionsabout new data. Throughout the specification, the description uses AIlogic that deploys Deep Learning, in the form of Neural Networks, toidentify classes of objects, object locations in video images andsegments. Deep Learning is also used to identify distinctive activitiesor sub-activities within the video images and segments. In some forms,Statistics-based machine learning is used to characterize the motion ordirection of objects within the video images and segments.

Example aspects of an autonomous recording and processing system willnow be more fully described. Each of these example aspects are providedso that this disclosure is thorough and fully conveys the scope of theinventive concepts, features and advantages to those skilled in the art.To this end, numerous specific details are set forth such as examples ofspecific components and methods associated with the system to provide athorough understanding of each of the aspects associated with thepresent disclosure. However, as will be apparent to those skilled in theart, not all specific details described herein need to be employed, theexample aspects may be embodied in many different forms, and thus shouldnot be construed or interpreted to limit the scope of the disclosure.

Various aspects of the disclosure may refer generically to hardware,software, modules, or the like distributed across various systems.Various hardware and software may be used to facilitate the features andfunctionality described herein, including, but not limited to: an NVIDIAJetson TX2 computer, having a 256-core NVIDIA Pascal GPU architecturewith 256 NVIDIA CUDA cores, and a Dual-Core NVIDIA Denver 2 64-bit CPUand Quad-Core ARM Cortex-A57 MPCore, including 8 GB 128-bit LPDDR4memory and 32 GB eMMC storage. Software may include: Linux operatingsystem having Python programmed applications; OpenCV image processinglibrary; AWS Greengrass ML Model Development and Execution; videoediting software using OpenCV image processing library and Pythonprogramming. Various cloud services and for storing and sending videomay be used, including AWS S3 and AWS Glacier for video storage, and AWSCloudFront for content delivery and distribution. Cloud services forprocessing and editing video may include Python and OpenCV running onAWS EC2 servers. Cloud services for converting videos from one format toanother may include AWS Elemental MediaConvert. Cloud services and AIfor generating a Neural Network may include AWS SageMaker forconstructing, training, tuning, and evaluating machine learning models,including Keras/TensorFlow developmental framework, and Sagemaker NEO toprepare models for deployment to local computers.

Cameras for use in the systems described herein may be HD cameras or 4Kcameras.

A 4K camera may be manufactured by Hanwha, Model PNP-9200RH havingspecifications and operating manual herein incorporated by reference.Hanwha camera model PNO-9200RH is a 4K PTZ Camera including thefollowing specifications:

-   -   Imaging Device Sensor: 1/2.5″ 8MP CMOS    -   Resolution: 3,840(H)×2,160(V), 8.00M pixels    -   Focal Length (Zoom Ratio): 4.8˜96 mm (Optical 20×)    -   Angular Field of View H/V: 65.1 deg. (Wide)˜ 3.8 deg.        (Tele)/38.4 deg. (Wide)˜ 2.2 deg (Tele)    -   Auto-Focus & Auto-Iris    -   Infrared Illumination    -   120 db Dynamic Range    -   Pan Range/Speed: 360 degrees/400 degrees per sec    -   Tilt Range/Speed: 190 degrees/300 degrees per sec    -   16×Digital Zoom    -   Application Programming Interface: ONVIF Profile S/G    -   Video Compression Formats: H.265/H.264, MJPEG    -   Max. Framerate H.265/H.264: 30 fps at all resolutions    -   Audio In Selectable (Mic in/Line in)    -   Ethernet: 10/100 BASE-T    -   Operating Temperature/Humidity: −58° F.˜+131° F./less than 90%        RH    -   Ingress Protection: IP66/Vandal Resistance IK10    -   Input Voltage: 24V AC    -   Power Consumption: 90 W (Heater on, IR on)

A camera may be an HD camera capable of recording in High Definition. Assuch, a camera may be a Hanwha HD 1080p PTZ camera having a Model NumberXNP-6321H with specifications and operating manual herein incorporatedby reference. Hanwha camera Model XNP-6321H is a HD 1080p PTZ Cameraincluding the following specifications:

-   -   Imaging Device: 1/2.8″ 2.4M CMOS    -   Resolution: 1,981(H)×1,288(V), 2.55M    -   Focal Length (Zoom Ratio): 4.44˜142.6 mm (Optical 32×)    -   Angular Field of View H/V: 61.8 deg. (Wide)˜ 2.19 deg.        (Tele)/36.2 deg. (Wide)˜1.24 deg (Tele)    -   Auto-Focus & Auto-Iris    -   IR Illumination    -   150 db Dynamic Range    -   Pan Range/Speed: 360 degrees/700 degrees per sec    -   Tilt Range/Speed: 210 degrees/700 degrees per sec    -   Digital Zoom: 32×    -   Application Programming Interface: ONVIF Profile S/G    -   Video Compression Formats: H.265/H.264, MJPEG    -   Max. Framerate H.265/H.264: 60 fps at all resolutions    -   Audio In Selectable (Mic in/Line in)    -   Ethernet: 10/100 BASE-T    -   Operating Temperature/Humidity: −31° F.˜+131° F./less than 90%        RH    -   Ingress Protection: IP66/Vandal Resistance IK10    -   Input Voltage: 24V AC or POE+    -   Power Consumption: Max. 24 W (Heater Off), Max. 65 W (Heater on,        24V AC)

Referring now to FIG. 1 , a block diagram illustrating a videoprocessing system for detecting and recording activities is provided.Video processing system, generally illustrated at system 100. Forpurposes of discussion, various embodiments and aspects of system 100are described and illustrated herein, with various system modulesdistributed across different interconnected systems, hardware, andsoftware, which communicate with each other in a wired or wirelessmanner, both locally and remote over the internet/cloud. Variousfunctionalities of system 100 described herein may be accomplished withthe use of a computer, including processor and non-transitory computerreadable medium or memory, with instructions stored thereon to beexecuted by processor. System 100 may function automatically accordingto rules set forth in various algorithms. It will be further appreciatedthat the various processors, memory, and instructions may be distributedamong various systems and via the cloud, and that some instructions orprocessing may occur remotely and be communicated between systems ormodules.

According to an aspect, system 100 can include a first remote camera102, a second remote camera 104, a third remote camera 106 or variouscombinations of additional cameras illustrated generally as Nth remotecamera 108. System 100 also includes a network switch 110 connecting oneor more remote cameras 102-108 to AI enabled video processor 112. System100 includes non-transitory memory 111 connected to AI enabled processor112. Remote cameras 102-108 may be operatively connected to networkswitch 110 and can be controlled by AI enabled video processor 112. Inother forms, cameras 102-108 can work independently with on-boardcapabilities for recording video as described herein.

System 100 can also include modem 114, such as a cellular modem orhardwired modem, configured to communicate or transmit data via anetwork such as Internet/Cloud 116. Modem 114 can be a cellular modemcapable of communicating using a 3G, 4G, 5G, or other communicationstandard. In other forms, modem 114 can be a wired modem capable ofcommunicating using a broadband connection such as Ethernet or via aFiber Optic connection, or various combinations thereof.

According to an aspect, modem 114 may be configured to communicate rawvideo data captured by remote cameras 102-108 for further processing, ormodem 114 may be configured to communicate processed videos created byAI Enabled processor 112. Thus, according to an aspect, modem 114 may beconfigured to communicate with, or be in operative communication with,Internet/Cloud 116.

According to a further aspect, system 100 may further include a remotevideo processing and management system 118 connected to modem 114 viathe Internet/Cloud 116. Remote video processing and management system118 may process video automatically and manage distribution of videofiles created. System 100 may further include a content management anddelivery system 120 in communication with remote video processing andmanagement system 118. Content management and delivery system 120 may beconfigured to further control distribution of video created by system100.

For example, in response to receiving raw video data at remote videoprocessing and management system 118, the system 100 may be configuredto automatically process the video in accordance with predeterminedinstructions. In response to processing video, content management anddelivery system 120 may receive the processed video, and in responsethereto transmit or make available the processed video to an end user.As such, system 100 can use video processing located near a remotecamera to detect a video activity and process video in one or more formslocally to the camera, within a cloud service, or combinations thereof.

According to an aspect, system 100 can be used in a golf environment todetect a golfer and record and communicate a golfers activity in theform of processed video. During use of system 100 in a golf environment,system 100 may be configured to automatically capture video data of agolfer via cameras 102-108. In response to capturing video data, system100 may receive video data from cameras 102-108 at AI Enabled Processor112. AI Enabled processor 112 may automatically process video data andcreate a processed video. For example, AI enabled processor 112 can beused to detect a golfer using image data from the video and a neuralnetwork created to detect a person within a video or image frame. Upondetecting the person, a neural network (NN) can be created to identifyother elements of the golfer such as the golfer's clothes, shoes, a hat,a golf club, aa golf ball, or various other aspects or combinations ofaspects that are unique to a golf activity. Upon identifying the golfer,system 100 can capture and process video for that specific golfer. Inresponse to creating processed video, AI Enabled Processor 112 maytransmit video data. Processing of video data may occur locally orremotely. For example, AI Enabled Processor 112 may be part of cameras102-108, or at a computer or processing system in communication withcameras 102-108, or in the cloud.

According to another aspect, system 100 may be configured to detect thepresence of one or more golfers within a predetermined area associatedwith cameras 102-108. For example, system 100 is configured to detect,via signals sent to AI Enabled Processor 112, that a golfer has reachedthe hole on the golf course where cameras 102-108 have been installedand calibrated. System 100 is further configured to detect when one ormore golfers have completed the hole and have left the hole. In thismanner, a limited amount of video may be captured and recorded only whena golfer is present thereby reducing the amount of memory and processingneeded to store, process and communicate video.

According to a further aspect as described in further detail below, agolfer may have a transmitter associated with themselves, such as a GPSor Location Services enabled device such as a mobile device, smartphoneor tablet or other GPS or Location Services enabled device, an RFIDdevice, a Bluetooth enabled device, or any combination thereof, whichmay communicate with system 100 to indicate the presence of a golfer. Inanother form, system 100 may be configured to communicate with a golfcart having a GPS or Location Services tracking system integrated withinthe golf cart to detect the location of a golf cart and a golfer.

Thus, for example, prior to automatically recording video at cameras102-108, system 100 may receive a proximity signal from a deviceassociated with a golfer. In response to receiving a proximity signal,system 100 may automatically begin recording the hole via cameras.

According to another aspect of the disclosure, one or more of remotecameras 102-108 may include GPS or Location Services positioningfunctionality, such as a GPS or Location Services device installed inone or more remote camera 102-108, such that the GPS coordinates ofremote cameras 102-108 may be known. A GPS or Location Services devicecan be used to detect and transmit to system 100 specific GPScoordinates of each camera 102-108, thereby providing points ofreference to system 100 to provide system 100 with location data of eachremote camera 102-108, such that other objects on a golf hole and withinthe field of view of remote cameras 102-108 may be detected by thecameras 102-108. As such, system 100 can triangulate to determine alocation of an object relative to remote cameras 102-108.

According to another aspect, during an initial setup phase of system 100at a golf course, remote cameras 102-108 may be installed at fixedlocations determined by an installer at the golf hole, depending on thespecific layout of the golf hole. As is typical, each golf hole isunique, having a unique layout between the tee box and the green andareas in between, including different grass cuts (such as a fairway,first cut, rough, fringe, etc.). In one aspect, first camera 102 may bedisposed behind a tee box, and second camera 104 may be disposed behinda green. When installed, remote cameras 102-108 are typically placed ina position that is not in the expected path of the ball flight. Putanother way, first remote camera 102 can be located behind a golfer whena golfer is facing the green while standing on a tee box, and secondremote camera 104 can be located beyond the green in a directionrelative to a tee box. Accordingly, remote cameras 102-108 arepositioned such that an impact from the golf ball would be unlikely. Itwill be appreciated that system 100 can include multiple differentcameras at a variety of different locations.

Referring now to FIG. 2 , a block diagram illustrating an AI enabledcamera for use with a video processing system is provided. An AI EnabledCamera, illustrated generally at 200, includes on-board processor 204and one or more sensors 206, 208, 210, which may be configured to sendsignals and/or video to processor 204.

Sensors 206-210 can include optical sensors and in some form can includevarious types or combinations of sensors, including, but not limited to,optical, motion, infrared, radar or Doppler sensors, Bluetooth sensors,Wi-Fi Sensors, RFID sensors, or various combinations thereof. Camera 202may further include memory 212 in communication with processor 204, andan embedded AI module 214 in communication with the memory 212. The AImodule 214 may be in the form of software stored on memory 212 andexecuted by processor 204.

Camera 202 may further include a communication module 216 incommunication with processor 204 and a power module 218. Communicationmodule 216 may wired or wireless communication such as fiber optic,Ethernet, Power over Ethernet (POE), and the like. Wirelesscommunication can be Wi-Fi or other 802.11 communication, Bluetooth,cellular communication or various combinations thereof. Power module 218may be configured to provide power to camera 202. Power module 208 maybe in the form of a battery or hard wired input connected to a powergrid or other source of power. According to one aspect, power can beprovided using a POE connection sufficient to power camera 200 over agiven distance. Thus, in reference to the automatic video recording andprocessing steps described herein, camera 202 may perform these steps,and in response to creating the processed video, camera 202 may transmitthe processed video to the end user.

Camera 202 may further include a control module 220 in communicationwith camera sensors 206-210. Control module 220 is in communication withprocessor 204, and may receive signals from the sensors 206-210 andprovide signals to processor 204 for controlling camera 202. Controlmodule 220 may be configured to pan, tilt, or zoom camera 202. Camera202 may be used for any other cameras described herein. The variouscameras described herein may include some or all of the functionalityassociated with camera 202. For example, various cameras describedherein may include pan, tilt, and zoom functionality, but some may notinclude on board AI processing, for example. For example, in one aspect,in response to detecting one or more golfers, the camera 202 maygenerate a control signal for the control module 220 to tilt/pan, orzoom the camera 202 automatically.

According to an aspect, camera 202 may be a 4K camera manufactured byHanwha, Model PNP-9200RH having specifications and operating manualherein incorporated by reference. Camera 202 as Hanwha camera modelPNO-9200RH is a 4K PTZ Camera including the following specifications:

-   -   Imaging Device Sensor: 1/2.5″ 8MP CMOS    -   Resolution: 3,840(H)×2,160(V), 8.00M pixels    -   Focal Length (Zoom Ratio): 4.8˜ 96 mm (Optical 20×)    -   Angular Field of View H/V: 65.1 deg. (Wide)˜ 3.8 deg.        (Tele)/38.4 deg. (Wide)˜ 2.2 deg (Tele)    -   Auto-Focus & Auto-Iris    -   Infrared Illumination    -   120 db Dynamic Range    -   Pan Range/Speed: 360 degrees/400 degrees per sec    -   Tilt Range/Speed: 190 degrees/300 degrees per sec    -   16×Digital Zoom    -   Application Programming Interface: ONVIF Profile S/G    -   Video Compression Formats: H.265/H.264, MJPEG    -   Max. Framerate H.265/H.264: 30 fps at all resolutions    -   Audio In Selectable (Mic in/Line in)    -   Ethernet: 10/100 BASE-T    -   Operating Temperature/Humidity: −58° F.˜+131° F./less than 90%        RH    -   Ingress Protection: IP66/Vandal Resistance IK10    -   Input Voltage: 24V AC    -   Power Consumption: 90 W (Heater on, IR on)

Alternatively, camera 202 may be provided as an HD camera capable ofrecording in High Definition. As such, camera 202 can include a HanwhaHD 1080p PTZ camera having a Model Number XNP-6321H with specificationsand operating manual herein incorporated by reference. Camera 202 asHanwha camera Model XNP-6321H is a HD 1080p PTZ Camera including thefollowing specifications:

-   -   Imaging Device: 1/2.8″ 2.4M CMOS    -   Resolution: 1,981(H)×1,288(V), 2.55M    -   Focal Length (Zoom Ratio): 4.44˜142.6 mm (Optical 32×)    -   Angular Field of View H/V: 61.8 deg. (Wide)˜ 2.19 deg.        (Tele)/36.2 deg. (Wide)˜1.24 deg (Tele)    -   Auto-Focus & Auto-Iris    -   IR Illumination    -   150 db Dynamic Range    -   Pan Range/Speed: 360 degrees/700 degrees per sec    -   Tilt Range/Speed: 210 degrees/700 degrees per sec    -   Digital Zoom: 32×    -   Application Programming Interface: ONVIF Profile S/G    -   Video Compression Formats: H.265/H.264, MJPEG    -   Max. Framerate H.265/H.264: 60 fps at all resolutions    -   Audio In Selectable (Mic in/Line in)    -   Ethernet: 10/100 BASE-T    -   Operating Temperature/Humidity: −31° F.˜+131° F./less than 90%        RH    -   Ingress Protection: IP66/Vandal Resistance IK10    -   Input Voltage: 24V AC or POE+    -   Power Consumption: Max. 24 W (Heater Off), Max. 65 W (Heater on,        24V AC)

As such, camera 202 may be realized as various different types ofcameras and may be deployed and used with the various video processingsystems and methods described herein.

Referring now to FIG. 3A, a block diagram illustrating an AI enabledvideo processing system is provided. An embedded AI video processingsystem, illustrated generally as system 300 includes processor 302 andmemory 303. System 300 can include an NVIDIA Jetson TX2 system toprocess and control local video cameras. Processor 302 can include aDual-Core NVIDIA Denver 2 64-Bit CPU and Quad-Core ARM® Cortex®-A57MPCore, memory 303 can include 8 GB 128-bit LPDDR4 Memory and can alsoinclude 32 GB eMMC of storage. System 300 can also include AI enabledgraphics processor 316 which can include 256-core NVIDIA Pascal™ GPUarchitecture with 256 NVIDIA CUDA cores. Operating software for system300 can include a Linux operating system, Python as an applicationprogramming language, and OpenCV image processing Library. System 300further includes AI logic module 318 including a Machine LearningDeployment and Execution software such as Amazon Web Services GreengrassML software.

System 300 further includes a remote camera interface 304 incommunication with processor 302. Remote camera interface 304 may beconnected to network switch 110 illustrated in FIG. 1 , or otherinterfacing/communication mechanism that connects camera processor 302.System 300 may further include a power module 306 and configured toprovide power to processor 302 and other components of system 300. Powermodule 306 may be in the form of a battery, or may be a hard-wiredconnection to another source of power, such as a power grid or existingpower source. Remote camera interface 304 can be used to power remotecameras connected to remote camera interface 304. According to oneaspect, remote camera interface 304 can be an Ethernet interface andeach of the cameras (not illustrated) may be powered and controlledusing a PoE connection. Other forms of connection can also be usedincluding, but not limited to fiber optic, coaxial cable, twisted pair,single strand or various combinations thereof.

System 300 may further include communication module 308 connected toprocessor 302 and a modem such as cellular modem 310, configured totransmit data. Modem 310 may be the modem 114 of FIG. 1 or other formsof modems as needed. Modem 310 can communicate with a wireline orwireless network capable of connecting the Internet or Cloud basedservices. Communication module 308 can be used to determine the locationor address to communicate with via modem 310, and may further receivedata or instructions for processor 302 via the modem 310.

Additionally, according to an aspect, system 300 may further include AIenabled digital video recorder 312 and local video processing engine314, each of which may be connected to processor 302 and may receivecontrol signals from processor 302. Video recorder 312 and processingengine 314 may combine to receive raw video and store raw video, and maythen process raw video automatically to detect specific objects andcreate specific types of video described herein.

During use, system 300 can set up and control cameras using remotecamera interface 304, to capture and record video. AI Enabled graphicsprocessor 316 can perform object detection on the recorded video toidentify a predetermined activity. If the predetermined activity isvalid, system 300 can process the video into segments for a detecteduser. System 300 can edit the segments, as needed, and combine thesegments into a video file uniquely for the detected user. The videofile may then be uploaded to the Internet or Cloud using communicationmodule 308 and cell modem 310. In this manner, local video processingusing AI capabilities may be deployed at a location thereby reducing theoverall file size of a video file, generating user specific content, andefficiently communicating video files to the Internet or Cloud forexpedited access.

According to a further aspect, system 300 can provide AI videoprocessing locally near a geographic location of an installed remotecamera to provide automatic monitoring and processing of a desiredactivity. Thus, the automatic detection and video recording/processingmay be performed by system 300, and the processed video may betransmitted to an end user. The processed video may be received andforwarded to the end user via an intermediate server or othercommunication device.

However, in another aspect, system 300 may use remote AI videoprocessing, as further discussed below. When processing video remotely,video may still be recorded locally by one or more remote camera.Recorded video can then be communicated by system 300 to a remoteprocessing system (not expressly illustrated) to edit and incorporateadditional graphics automatically using remote video processing systemsuch as remote video processing system 400 described below.

FIG. 3B is a flow diagram of a method for local video processing inaccordance with an aspect of the present disclosure. The method may beused by one or more of the systems, devices, processors, modules,software, firmware, or various other forms of processing to carry outthe method described in FIG. 3B. Additionally, FIG. 3B may be realizedas within various portions of FIGS. 1-3A, 4-8 , and in some aspects, maybe modified to include various functions, uses, and features describedtherein.

The method begins generally at step 301 and can be used at variousgeographic locations where a predetermined activity is to be performed.As one example, the method can be used for local processing and videocapture on a golf course as described below although other activitiescan be described in connection with the method of FIG. 3B. The methodproceeds to step 303 when a presence is detected. For example, a golfermay be detected as they approach a tee box in connection with playing agolf hole. Detection can occur in a variety of ways including, but notlimited to using receiving a signal from another transmitter, such asGPS or RFID, Wi-Fi, Bluetooth, location services within a mobile deviceor other sensors to detect a presence including motion detection, RFIDdetection, Bluetooth detection, Wi-Fi detection, Radar sensing, thermalor heat sensing or various other sensing technologies. In addition todetecting the presence of the individual golfers, similar transmittersmay be provided on a golf cart or the like, to indicate the presence ofone or more golfers. It will be appreciated that other detectionmechanisms may also be used.

Upon sensing a presence, the method proceeds to step 305 to determine ifa user is valid. For example, a presence detected may be a deer on agolf course or a jogger or walker. As such, a golfer can be detected atstep 305 using various types of techniques. For example, in one form, agolfers location services of a mobile device having an application forrecording video can be detected. For example, a geofence can be placedon a tee box and when a valid golfer approached the tee box, thegeofence will trigger to validate the user. Other forms of validationcan also be used such as AI Logic that includes facial recognition ofthe golfer and detected by a camera at the location. In another form, AIlogic can include an object recognition Neural Network capable ofidentifying objects that are unique to golfers. For example, the AIlogic can identify a person and a golf club on the tee box. Other formsof object recognition can be used to identify a golfer as describedherein.

If a user is not valid, the method proceeds to step 303 until anotherpresence is detected. If at step 305, a valid user is detected, themethod proceeds to step 307 and initiates recording the activity. Forexample, multiple cameras may be present on a golf hole or otheractivity location and can be used to record an activity. As such, when avalid user is detected, the remote cameras associated with a geographiclocation can begin recording the event. In one form, the recording canbe initiated by an individual as well. For example, a golfer may have amobile device with an app associated with the cameras located at thegeographic location. As such, the golfer may step onto the green andinitiate recording the golf activity. In this manner, the recording canoccur either automatically or through the use of a user initiating therecording.

The method then proceeds to identify the user at step 309. For example,at step 309 a golfer may have been detected and at block 309, a user canbe identified during the recording. For example, the method can useimage processing to identify the clothing a user is wearing and in otherforms, AI logic can be used to identify the specific user using facialrecognition. Upon a user being identified, the method can proceed tostep 311 and identify the activity. For example, a golfer with a golfclub can be used to identify the activity within one or more frames of avideo segment. In some forms, other individuals with golf clubs mayappear on the tee box that have not subscribed to a service providedwith the method of FIG. 3B. As such, if an invalid user is swinging agolf club, the method would not count the activity as being valid andwould proceed to step 303. In other forms, a maintenance worker may bepresent on the tee box doing work on the tee box. The method wouldidentify the activity and at step 313, use AI Logic having validactivities dismiss the activity as being invalid. The method would thenproceed to step 303 until another presence is detected. If at step 313 avalid activity is detected, the method proceeds to step 315 andcontinues to record the video.

Upon continuing recording, the method would check at step 317 todetermine if the user is still present at the geographic location. Forexample, the user may be a golfer and can strike the ball multiple timesbefore completing the hole. As such when the user leaves a predeterminedregion of the geographic location, such as the green, the recording willend. If the user has not exited or left, the remote camera(s) willcontinue to record. If at step 317 the user is no longer present ordetected, the method proceeds to step 319 where the video is processedfor the user.

At block 319, local processing of video can include a variety of AIlogic processing, image process, formatting and compression techniquesas described herein. According to one aspect, the video can be segmentedinto portions that include only the identified user. Segmentation canoccur using AI Logic or image processing to validate the user within thesegments. Once the segments have been created, additional information,such as ball tracing and predetermined graphics, can be added to thevideo segments. The video segments can then be merged together andformatted to a format that can be used by and end user. According toanother aspect, the final video may be compressed prior to communicatingfrom a local video processor. In other forms, various portions ofprocessing can be added or removed depending on the desire of the methodto perform local processing.

Upon processing the video, the method proceeds to step 321 and detectsif another user is present. For example, the video may be of asnowboarder going down a trail with multiple friends boarding with himor her. The method would then detect another valid user in the video andextract the segments of the video where the second snowboarder ispresent. Upon extract the segments for the additional user, the methodcan proceed to step 323 and process the video for the second user. Forexample, the processed video for multiple snow boarders may include atrace or colored line detailing where the second snowboarder descendedrelative to the first snowboarder. In this manner, multiple users can bedetected using the same video and a unique video may be segmented forthat user. Although as shown as only a second user, it should beunderstood that the video can be processed to detect multiple additionalusers as needed or desired. Upon processing the second user video themethod proceeds to step 325 and communicates the formatted video and tostep 327 where the method ends.

Referring now to FIG. 4A, a block diagram illustrating an AI enabledvideo processing system for use remotely is provided according to anaspect of the present disclosure. The AI enabled video processingsystem, illustrated generally as remote video processing system andmanagement system or remote processing system 400, includes a networkprocessor 402 connected to cloud storage and services 404, which isconnected to a remote video interface 406 capable of communicating videofrom a remote camera (not expressly illustrated). Network processor 402can access various modules for managing and processing video receivedfrom remote video interface 406. For example, network processor canaccess a remote video manager 416, a content manager 418, a profilemanager 420, a format manager 422, and an output manager 424. Eachmanager listed can be provided as a software module or program interfacethat can be accessed as needed by network processor 402.

Network processor 402 can also be realized as a cloud service that canbe deployed using Amazon Cloud Services, IBM Cloud Services, MicrosoftCloud Services, or various combinations thereof. Cloud Storage andservices 404 and distribution manager/communication 408 can also includevarious types of cloud storage services and distribution services havingdifferent storage capabilities and accessibility. For example, somecontent may be stored for immediate access while other forms of contentcan be stored for delayed access using a deep storage technique. Thiswill enable flexibility in access to content such as video whilereducing the overall cost on a user by user basis. For example, if auser has elected to pay for longer term storage, image processing systemcan modify the type of storage on a rate by rate basis. As such, cloudstorage and services 404 can include various different types of on-lineservices and according to one aspect, can include Amazon Web Services(AWS) Glacier for storing video in the cloud. Additionally, Contentmanager 418 and distribution manager/communication 408 can utilize AWSCloudfront as a content delivery service that distributes videos to endusers.

Remote system 400 can also include an AI Enabled graphics processingengine or GPU 410. GPU 410 can include various types of AI enabledprocessors and in one form, includes one or more NVIDIA V100 Tensor CoreGPU capable of AI processing to generate and develop and train a MachineLearning (ML) for AI Logic 412 that can be created, modified,distributed and used by system 400 or other AI enabled processorsdescribed herein. According to one aspect, GPU 410 and/or networkprocessor can also utilize additional software and services to create AIlogic 412. For example, GPU 410 can use AWS SageMaker for constructing,training, tuning, and evaluating ML models. Sagemaker supports a numberof ML development frameworks and, according to one aspect, may useKeras/TensorFlow. Additionally, system 300 can employ Sagemaker NEO toprepare the AI Logic 412 models for deployment to remote processors asillustrated is FIGS. 1-3, 6 .

According to an aspect, GPU 410 can access graphical assets 414 that canbe added to video that is being processed using network processor 402.GPU 410 can also access AI logic 412 that can include a variety ofstored AI enabled logic that are designed to automate various aspects ofautonomous video processing at remote processing system 400 and localprocessing system 300, camera 200, or various other processing systemsand devices provided herein. For example, AI Logic 412 can be createdusing various videos created during a specific activity such as a golfactivity, football activity, soccer activity, baseball activity,basketball activity, skiing activity, snowboarding activity, bikingactivity, fishing activity, boating activity, general sports activities,or various other types of non-sports activities that are predeterminedto occur at a geographic location. AI Logic 412 can process thepreviously recorded image data within a video frame and can be used totag objects within the video that are important or relevant to theactivity. For example, a AI Logic 412 can be used to tag a footballbeing used within a football field, but may not be used to tag a birdflying over the football field. Further, a football player's number andname can be processed and tagged in a way to aid in identifying video inwhich the specific player may be present and processed accordingly. AILogic 412 that is created for a specific activity can be shared with alocal video processing system or camera as described herein. In otherforms, AI Logic 412 can be stored locally in remote video processingsystem 400 to be used or distributed as needed.

According to a further aspect, system 400 can be used to post processedvideo received from remote video interface 406. For example, a video maybe modified or edited to add additional assets 414, or formatted using aspecific format provided by format manager 422. As such, system 400 canemploy additional software for post processing and editing including theuse of Python and OpenCV for editing videos on AWS EC2 web servers.System 400 can also utilize a AWS Elemental MediaConvert to convert orformat video prior to distribution using distributionmanager/communications 408.

Referring now to FIG. 4B, a flow diagram of a method for processingvideo using AI enabled remote video processing in accordance with anaspect of the present disclosure is shown. The method may be used by oneor more of the systems, devices, processors, modules, software,firmware, or various other forms of processing to carry out the methoddescribed in FIG. 4B. Additionally, FIG. 4B may be realized as withinvarious portions of FIGS. 1-3B and 5-8 , and in some aspects, may bemodified to include various functions, uses, and features describedtherein.

The method begins generally at step 401. At step 403, when video isreceived form a remote video source, the method proceeds to step 405 toidentify an activity within the video. Various activities as describedherein can be stored within various AI logic that has been created usingMachine Learning as a Neural Network. Portions of the video can becompared to the AI logic and if an activity is not detected, the methodcan proceed to block 407 and process the video to identify a newactivity. In some forms, processing can include tagging or identifyingobjects within the video that are unique to an activity and can be usedby the Machine Learning for one or more activity. Upon processing thevideo, the method proceeds to block 409 to determine if a new activityshould be created within the Neural Network. For example, variousactivities as described herein can be identified but in some forms, asub-activity within an activity category can be created as well. Anexample of this activity can include, in one form of a golf activity, agolfer slicing or hooking a ball, a golfer throwing a club, a golferhigh flying another golfer, a golfer making a hole-in-one, or variousother activities or sub activities that may be created. If an activityshould be created, the method proceeds to step 411 and identifies theobject or series of objects that can be used and exist within an imageframe of the video. The method can proceed to step 423 and label theobject(s) identified and then to block 425 where the object or frame canbe added to the AI logic for that activity. In some forms, if theactivity exists, the object can be added to the Neural Network of theactivity, and in other forms, if the activity does not exist and aNeural Network is not available, the method can generate a new NeuralNetwork and Machine Learning instance to be used within the AI Logic.The method then proceeds to step 417 and processes the AI Logic and tostep 429 to determining if the activity is valid and can be releasedwithin the AI Logic. For example, the accuracy of a Neural Network caninclude dependencies on the number of objects identified and provided tothe Machine Learning instance for that activity. If only one instanceexists, the AI Logic will likely fail. As additional objects areidentified and used within the Machine Learning instance, the AI Logichas a statistically better chance of identifying the activity. Ifadditional objects for that activity are needed, the method proceeds tostep 401 until additional video is received. If at step 419 the activityis now valid, the Machine Learning instance can enable AI logic for thatactivity at step 421, and proceed to step 423 to distribute the AI Logicto various locations as needed. The method then proceeds to step 425 andends.

If at step 405 an activity is identified, the method proceeds to step427 and determines if the video is valid to output or store. Forexample, a local video processor may have processed the video sufficientfor distribution. As such, a remote video processor, such as system 400can detect if the video requires any additional processing using dataprovided with the video. If the video is valid to output, the method canproceed to step 429 and format the video using a format manager. Forexample, a video may need to be formatted to be output to a mobiledevice or application having specific formats, file size, and otherspecifications required in connection with posting a video. Videoprovided to various locations and applications can include Facebook,YouTube, Instagram, Snapchat, and other applications. Each app beingutilized may require its own formatting for publishing into a specificnetwork. As such, a format manager can determine one or more locationsfor the video and format accordingly. In other forms, the video can beprocessed to be distributed to a network location having a highdefinition or 4K video output on a stationary output device such as aspecific monitor. Various types of formatting may be used for the videoto output to various destinations. Upon formatting the video, the methodproceeds to step 431 and distributes the formatted video using adistribution manager. For example, the video may be a single instancethat is distributed to a cloud storage account configured to store thevideo. However in other forms, the video may have been formatted intomultiple formats, thus creating multiple videos that may need to bedistributed. As such, at step 431 the video is distributed to thosedestinations. The method then proceeds to step 425 and ends.

If at step 427 the video is not valid to output, the method proceeds toprocess the video. For example, the method includes 3 different types ofprocessing that may be used to process the video and are provided in noparticular order but only as a reference for illustrating processing ofthe video. At block 433, the method determines if one or more userprocessing needs to be performed. For example, a local video processormay have provided information for a specific user recorded in the video.As such, that information can be used to identify the user within thevideo. Various types of identification can be used including facialrecognition, geofencing, GPS or Location Services locationidentification, grid identification, manual input from a mobile app ofthe user, or various other triggers that can be used to identify thespecific user within the video. The method can also use AI Logic toidentify the specific user and characteristics, details, and/or objectsof that user can be provided with the video. Upon identifying the user,the method proceeds to step 437 and extracts segments of video as theyrelate to the user. For example, a user that is identified may be afootball player having a specific jersey number and name. The methodwould locate all segments of the video where the football player ispresent, and extract those segments from other players. In another form,a golfer may be playing a hole on a golf course with other players andthe video may include numerous other shots or activities taken by theother golfers. As such, the method can identify the specific user andactivity within various segments of video and remove the segments thatdon't include the user. In this manner, a video of just the golfer canbe created. Upon extracting the video segments, the method proceeds tostep 439 to determine if the end of the video has been reached. If ithasn't, the method proceeds to step 437 and repeats. If the video hasended, the method proceeds to step 441 and determines if the videoshould be processed for a new user. For example, as mentioned multiplegolfers or players may be a part of the same video captured. As such,when desired a new user can be identified at block 435 and the methodcan proceed as described above. In this manner, multiple segments thatare unique to a specific user can be extracted from a single videothereby reducing the number of video uploads needed for processing. Forexample, on a football field, a single video can be uploaded and themethod can extract the video footage for each player thereby creatingunique video segments for each player that can be provided to eachplayer, their teammates, coaches, and the like. Although at block 441multiple users may be detected, the method may not desire to extractvideo segments for all users and may include a profile from a profileand content manager to extract only certain user's segments.

If at step 441 the method determines that no additional user segmentsshould be extracted, the method can proceed to step 443. At step 443,the method determines if the segments require further processing. Forexample, if only a single segment of video is extracted, no additionalprocessing to combine segments may be needed. If at step 443 the videosegments require additional processing, the method proceeds to step 445and combines the segments for each user into a single video. Forexample, the segments can be extracted and stored as portions of a videoor video segments. At step 445, the segments can be combined together tocreate a single composite video for a user. Upon combining the segments,the method proceeds to step 447 and combines video segments for anyremaining user to create a video unique to each user. As such, anindividual participant can have their own video with segments createdfor their unique experience.

Upon processing the video if needed, the method proceeds to step 449 anddetermines if any effects need to be added to the video. For example, acontent manager, such as the content manager 418 in FIG. 4 or otherautonomous content manager, may identify a video of a golfer that wasplaying a certain golf hole at a resort such as Omni Barton Creek. Thecontent manager may have stored an introductory video of a drone flyoverof the golf hole being played and may add the introduction video to theuser's video segment. In other forms, animated graphics illustrating thedistance to the hole can be drawn from a tee box to the green from a‘top down’ view of the hole. Other effects can also include adding audioor additional captured video of the user and other players at theactivity. In one form, portions of a segment of the video may beidentified or tagged to add a tracer to the movement of the ball as apart of creating effects. In other instances, AI Logic can be used todetect when a ball is located around the green in a location that is notdesired by the golfer. In that instance, an augmented effect can beadded to the video when a ball goes into the woods, a sand trap, a waterhazard and the like. An augmented effect can include an animated videooverlay. For example, an animation of a Loch Ness Monster stealing thegolf ball as it enters the water hazard can be added to the videosegment. Other animations can also be used and added as needed ordesired. In this manner, an augmented reality can be added to the videofor the user. According to a further aspect, the video may add a balltracing effect to a shot made by a golfer. For example, the method canbe used to identify a golf ball within the frames of the video and add acolored trace line to each frame to show the path of the ball. If thevideo includes video segments of the ball coming into the green, thetrace can be added to the video as ball lands onto the green. In someinstance, AI logic or image processing can be used to locate the ball ina frame and, in some cases, the video may be reversed after the ball islocated on the green. For example, when a user approaches his or hergolf ball on the green, AI logic or image processing can identify theuser and add effects or other content prior to the user picking up theball or addressing the ball. In this manner, through reverse processingof the video data, the ball can be traced back in previous frames orsegments and the video can be modified for that specific useraccordingly. In another form, an effect can include audio effects,music, or sound added to the video. For example, music can be addedthroughout all or portions of the video and can include various audiolevels. Unique sounds can also be added to the video based on what ishappening in the video. For example, a user may hit the ball into thewoods and a ‘chainsaw’ sound, clapping sound, laughing sound, applaudingsound or other sound effect can be added to the video segment. Inanother form, AI Logic or image processing can be used to identify whena golf ball goes in the cup and a ‘ball dropping in the cup’ soundeffect can be added. Effects can be predetermined based on the activityor sub-activity identified by the AI logic. In this manner, the methodcan access a label within a video segment and automatically add theeffect desired to portions or segments of the video.

After adding an effect if desired, the method proceeds to step 453 anddetermines if graphics need to be added to the video. If no additionalgraphics need to be added, the method proceeds to step 429 and ends. Ifadditional graphics are to be added, the method proceeds to step 455 andobtains the content or graphics to be added from and assets resourcesuch as assets 414 using content manager 418 of FIG. 4 or other asset orcontent resources as needed or desired. Assets or graphics can includeone or more graphic to add to a video image or video segment. Forexample, graphics can include information such as the name of thegolfer, the date, the golf course, the hole #, the distance to the hole,the club used by the golfer, current weather conditions, the max heightof the ball or after it is hit, the speed of the ball after it is hit,the curvature of the ball during flight, the max distance the balltravelled, the current stroke or number of strokes taken, the par forthe hole, other player info currently playing with, or other playerinformation or course information as needed or desired. According toanother aspect, the golf course can include graphical assets to be addedto a segment of the video such as the name of the golf course, a logo ofthe golf course, the age or when established, the current pro's name,the owners name, or various other types of marketing assets or graphicsthat a golf course may desire to be added to a video segment. Althoughdiscussed as adding assets for the golf industry, other graphical assetscan be added to the video as needed or desired. Upon obtaining thegraphical assets the method proceeds to step 457 and modifies thesegments adding the assets or graphics to specific video image orsegments. The method then proceeds to step 429 where the method ends.

The method of FIG. 4B can be modified as needed to combine or removevarious portions as needed. For example, upon identifying an activity orsub-activity at step 405, the method can be used to segment video andfurther process the video segments to identify a sub-activity. Thesegment can be labeled as having that sub-activity and a label can befurther used to process the segments, add effects, add graphics, orvarious other types of processing of the video segment. In this manner,an automated process using AI Logic can efficiently edit and process avideo without the need for having an individual modify and edit a videomanually.

Thus, the system 100 may ultimately capture, process, and store videosof the activity for later provision to a user, such as via a user app500.

With reference to FIG. 5 , a schematic representation of user app 500and/or user phone 501 is illustrated, illustrating various screens andsubscreens presented by the app 500 for visualization by the user. Theapp may include a plurality of soft buttons 514, 516, 518, 520, and 522,corresponding to user Videos, user Locations, user Friends, user'sCoaching, and Other, respectively. By selecting the button 514 (“V” forVideo), a list of videos categorized by event type may be displayed. Asshown in FIG. 5 , the categories may include golf courses 502, skiing504, football practice 506, fishing 508, and soccer 510.

By selecting button 516, multiple location categories may be displayed.As illustrated in the example of FIG. 5 , the locations may includefirst, second, third, fourth, and fifth locations 524, 526, 528, 530,and 532, respectively. The illustrated locations include golf courses,ski resorts, marine locations, and athletic fields, for example.

By selecting button 518, a list of the user's Friends on the app may bedisplayed, including first, second, third, fourth, and fifth friendcategories 534, 536, 538, 540, 542. By selecting button 520, coachingcomments may be listed according to category, including first, second,third, and fourth categories 544, 546, 548, 550.

It will be appreciated that additional buttons and correspondingsubscreens may be used for other groups. Within each category, the appmay display a quantity of videos within the category. The quantity mayrepresent total number of videos, total number of unviewed videos, orother measure. In addition to organizing and displaying videos for theuser, the app 500 may also communicate, via software/hardware of thephone 501, with other aspects of the system 100, either directly orindirectly.

Referring now to FIG. 6 , a block diagram illustrating one example of AIenabled video recording system disposed at a golf course according to anaspect of the present disclosure is provided. The AI enabled videorecording system, illustrated generally as system 600, is used todetect, record, and process golf activity at a golf hole having tee box602 and green 604. System 600 includes cameras 606, 608, 610 that may bedisposed adjacent tee box 602, with camera 606 disposed behind tee box602, and cameras 608 and 610 disposed on opposite lateral sides of teebox 602. Cameras 614, 616, 618 may be disposed adjacent green 604, withcamera 616 behind green 604 (and facing teebox 602), and cameras 614 and618 on opposite lateral sides of green 604. Each camera is incommunication with camera interface 624, which may include networkswitch 110 of FIG. 1 , remote camera interface 304 of FIG. 3 , or otherinterfaces capable of connecting remote cameras to processor 611.Interface 624 is connected to or integral with processor 611 and AIvideo processing engine 622. Processing engine 622 may be processingsystem 112 or system 300 described above, and may include modem 310 ormodem 114.

Processing engine 622 may be connected to golf course irrigation powerinterface 620, which may provide power; however, other forms of powermay be provided to power processor 611 and processing engine 622.Processing engine 622 may be in communication with remote golf coursevideo processing and management system 628, which may be system 400.Video processing and management system 628 may be in communication withmobile app 630, which may be mobile app 500 described above. Mobile app630 may be installed on mobile device 631. Mobile device 631 may be amobile phone, tablet, smart watch, golf cart, pull art, push cart,powered “follow-me” carts, or any other mobile device. It will beappreciated that mobile app 630 may also beinstalled/embodied/accessible on other devices, such as traditionalcomputers, internet browsers, or the like. Mobile app 630 may alsoinclude other features and functionality as described below. It will beappreciated that the various above-described systems may be integratedinto system 600 in whole or in part, allowing for local and/or remoteprocessing of video captured by the cameras. Such processing may beaccomplished automatically based on data received by system 600 anddetermined using video or image processing and/or artificialintelligence. Further, various aspects and use of system 600 may berealized as methods and software capable of being used by system 600 orvarious components within system 600. As such, the description of FIG. 6or elements thereof can be deployed as methods.

Various cameras have been described above in reference to differentfigures and aspects of the cameras. For the purposes of discussion, eachof the cameras described above may be referred to generally as camera orcameras 601, which are illustrated generally in FIG. 6 . It will beappreciated that a reference to camera(s) 601 may also refer to cameras102-108, camera 202, cameras 606-618, or other cameras reference in thisdescription.

During an initial setup, cameras 601 may be utilized to record orperform a 3D scan of the hole/golf course, such that different aspectsof the course may be determined via image processing at processor 611.For example, processor 611 may be configured to detect the location andshape of a water hazard or sand trap/bunker, as well as the location oftrees, different cuts of grass, structures, and the like. The result ofthe 3D scan may be stored at processor 611 for the particular hole, andmay be used as a reference for later processing of ball flight.

As part of the initial setup, the heights of cameras 601 may bedetermined and entered into processor 611. This information may not bereadily acquired from GPS or Location Services location information ofcameras 601. The heights of cameras 601 may be manually entered, or maybe detected using other sensors utilizing other measuring methods, suchas lasers. Either by way of GPS coordinates or other measuring methods,distances between cameras 601 may also be measured.

Additionally, with positions determined for each of cameras 601 and a 3Dscan of the hole, the setup may include establishing geofence 636 orother predetermined boundary assigned to the hole. Geofence 636 may bein the form of a boundary box or a complex curvature surrounding thehole, and may be made in reference to the established GPS coordinates ofcameras 601. Geofence 636 may be utilized to detect when a golfer hasentered the hole, by detecting whether the golfer's position is insidethe boundary or outside the boundary of geofence 636.

Golfer detection may be based on a detected or transmitted location ofthe golfer, or via detection by cameras 601, or through other detectionmethods such as a sensor coupled with image processing software.According to another aspect, golfer detection may be based on receivinga signal from another transmitter, such as GPS or Location Services orRFID, Wi-Fi, Bluetooth, or the like, and may be transmitted via mobiledevice 631 or other transmitting device. In addition to detecting thepresence of the individual golfers, similar transmitters may be providedon a golf cart or the like, to indicate the presence of one or moregolfers. As described above, mobile device 631 may be a phone or otherdevice, such as a golf cart, associated with the golfer. It will beappreciated that other detection mechanisms may also be used

In addition to the 3D scan of the hole, the initial setup may includeidentifying via other marking methods the precise positions of variousobjects on the course. In one aspect, the installer may travel to thelocation of a specific object and mark that object with a specific GPSor Location Services location, thereby providing an additional referencepoint for that object. This may be repeated to mark various objectsaround the course. FIG. 6 , for example, illustrates a hazard adjacentgreen 604.

Thus, system 600 may receive location information corresponding tocameras 601 and surrounding objects. In response to receiving thislocation information, system 600 may define geofence 636. Followingdefinition of geofence 636, system 600 may automatically detect and/ordetermine when a golfer is present within geofence 636 and the golfer'slocation relative to cameras 601.

System 600 may further define location grid 650. Location grid 650,partially illustrated in FIG. 6 , may be limited to the area withingeofence 636, or it may extend beyond geofence 636. Preferably, geofence636 is defined to be a space large enough to encompass a substantialportion of the area where the golf ball, and the golfer, are likely tobe present while playing the hole/course. Of course, it will beappreciated that the ball flight of a golf ball is unpredictable,especially in the case of recreational golfers, and that a golf ball orthe golfer who hit the ball may ultimately travel outside geofence 636while playing the hole/course.

Grid 650 may be defined by a plurality of grid boxes, such as a 3′×3′box that is repeated across the entire hole or substantially the entirehole. Each grid box will have a fixed position relative to cameras 601,and grid boxes may be utilized to provide information to processor 611about the specific location of the ball or the golfer while the hole isbeing played. The locations of the ball or golfer with reference to aspecific grid box may be used during the image processing by processor611 to provide the golfer with a specifically tailored video.

Accordingly, in response to receiving location information of cameras601 and location information of environmental objects, system 600automatically defines location grid 650. Following definition oflocation grid 650, system 600 may automatically detect the position ofthe golfer within location grid 650.

System 600 may include one or more mobile devices 631, which may includeapplications and associated memories/processors and may therefore alsobe referred to as mobile computing devices. For the purposes ofdiscussion, mobile computing devices 631 are referenced as mobiledevices 631. Mobile devices 631 have been described above as providinglocation and detection functionality for the golfers. However, mobiledevices 631 may also provide other communication and controlfunctionality. Mobile devices 631 are configured to communicate withprocessor 611 to provide various information about the golfer toprocessor 611 to enable processor 611 to properly monitor and record thegolfer and the golf shot. Mobile devices 631 may include GPS or LocationServices functionality, thereby indicating the location of mobile device631 and the golfer that has mobile device 631 in their possession.Mobile devices 631 may also communicate with the cloud or remote basedsystems. It will be appreciated that any device having GPS or LocationServices location capabilities, such as a GPS or Location Serviceswatch, including one with yardage capabilities, can be employed forpurposes consistent herein. The various functionalities of mobile device631 described herein may also be provided by multiple mobile devices631. For example, one mobile device 631 may be used for location whileanother mobile device 631 may be used for communication withcloud/remote systems to provide or receive other information.

In one aspect, for a group of golfers, each golfer may have their ownmobile device 631 that is specifically in communication with processor611. The communication between mobile devices 631 and processor 611 maybe direct or may be via another communication relay. With mobile devices631 in communication with processor 611, processor 611 may determine thespecific location of each mobile device 631 and each golfer, and canthereby determine when one or more of the golfers has enteredpredetermined geofence 636 such that monitoring and recording shouldbegin.

For example, when the group of golfers and their mobile devices 631enter geofence 636, their mobile devices 631, which are detecting theirlocation, can determine that the coordinates of mobile device 631 arewithin predetermined geofence 636. The coordinates of geofence 636 maybe communicated and stored in mobile device 631, such that mobile device631 may make the determination that mobile device 631 is within thepredetermined geofence 636. Accordingly, mobile device 631 may thencommunicate to processor 611 that mobile device 631 is present withingeofence 636. In this aspect, processor 611 is not actively monitoringfor the presence of the golfer. Rather, processor 611 receives a signalfrom mobile device 631.

In another aspect, processor 611 may actively monitor for the presenceof a golfer or mobile device 631. For example, mobile device 631 orother device may send a “ping” at predetermined intervals. Processor 611may “listen” for the ping, and once the golfer and mobile device 631 arewithin a predetermined range of processor 611, processor 611 willdetermine that the golfer and mobile device 631 have arrived at thehole/course, such that monitoring and recording may be begin.

System 600 is configured to identify each specific golfer that isparticipating, and when each golfer is participating. Typically, golferstake turns hitting shots. While cameras 601 will record the shots thatare occurring, processor 611 is configured to determine which shotbelongs to which golfer, such that the specific shots can be associatedwith the correct golfer. Accordingly, processor 611 is configured toidentify the golfer prior to each shot.

In one aspect, the golfer may indicate, via associated mobile device631, that he/she is the one that is about to hit their shot. The golfermay indicate that it is their upcoming shot via a button displayed onmobile device 631. Alternatively, another golfer in the group mayindicate via their mobile device 631 that a specific golfer in theirgroup is about to hit their shot. Processor 611 may then associate theresulting shot with the indicated golfer. This type of golferidentification may be described as manual golfer identification.

In another aspect, the identification of the golfer about to hit theirshot can be performed automatically by processor 611 and associatedprocessors and software. In one aspect, facial recognition software maybe used. In this approach, prior to hitting their shot, the golfer maystand in front of one of cameras 601, such that camera 601 may capturean image of the golfer and determine via the captured image which golferof the group of golfers matches the recognized face. The golfers mayhave their face recorded prior to the round, such that processor 611 mayaccess a database of the golfer's faces that are expected toparticipate.

In another aspect, the golfer may be identified by the clothes they arewearing. Typically, each golfer's clothing is unique within a group. Forexample, pants, shirts, hats, shoes, and the like may be recorded byeach golfer and subsequently detected by camera 601 prior to a golferhitting their shot.

In another aspect, processor 611 may utilize the location data of mobiledevices 631 to determine which golfer is about to hit. For example, ifone golfer is standing on the tee box and addressing the ball, and theother golfers are standing off the tee box or not addressing the ball,processor 611 may determine the particular golfer that is hitting theshot based on the locations of mobile devices 631.

In another aspect, the golfers may carry remote identifier 633 on theirperson, such as in a pocket, clipped to their belt, fixed to their hat,or the like. Remote identifier may be in the form of a GPS transmitteror a RFID tag. In the case of the GPS transmitter, remote identifier 633may actively transmit location data of the golfer to processor 611,which will receive the transmission and detect the location of thegolfer. In the case of an RFID tag, remote identifier 633 may bedetected by processor 611, which may transmit radio frequencies tolocate the positions of the golfers relative to processor 611 todetermine the locations of each of the golfers.

In another aspect, system 600 may store the ending location of the ballof each shot, and subsequent shots played from the stored location maybe strung together with the previous shot. Thus, even if a particulargolfer cannot be identified at one time or another, system 600 may stilldetermine that the shot belongs to that golfer based on where the shotoriginated relative to where a previous shot ended. In one aspect, suchdeterminations may be made based on a location within a video frame or alocation within grid 650.

Thus, in view of the above, system 600 is configured to identify eachindividual golfer prior to the golfer taking their initial shot from thetee box, as well as subsequent shots. Cameras 601 installed at the holewill record each of the shots and, based on the identification of eachgolfer, store each golfer's shot in a recording database with a uniqueidentifier for each golfer, such that each golfer's shot can be laterprovided to the correct golfer.

As is typical, the resulting shot from each golfer will be unique. Itwill be appreciated that a number of factors are present that affect theresult of a shot, including the golfer's swing, positioning, wind speedand direction, and the like. Accordingly, the resulting locations ofeach golfer's shot may be located at various positions on the hole,typically closer to the green than the tee box. Thus, recording ofsubsequent shots for each golfer can include recording from multiplecameras.

Cameras 601 may be configured to include zoom functionality, includingone or both of optical zoom and digital zoom. Cameras 601 may be furtherconfigured to have tilt and pan functionality such that cameras 601 maybe pointed at an identified target. Cameras 601 may each be pointed andzoomed at the golfer attempting their shot, including their initial shotas well as subsequent shots up to and including the final shot of thehole.

In an alternative aspect, one or more of cameras 601 may have a fixedviewpoint without tilt, pan, or optical zoom. In this aspect, cameras601 may be configured to capture everything within its view. The videomay be provided or analyzed as a whole, or segments/windows of the viewmay be isolated or cropped to isolate a particular golfer or shot beingplayed. The same video image may therefore be used for more than onegolfer that are within the view of camera 601 at the same time.

After initial tee shots, system 600 is configured to determine andidentify which of the golfers will hit the subsequent shots. Typicalgolf etiquette and rules dictate that golfers shall take their shots asdetermined by which golfer's ball is furthest from the pin. Recreationaland professional golfers typically adhere to this convention; however,exceptions are common, especially in a recreational setting. Many golfcourses encourage players to play “ready golf” in which the first golferthat is ready to hit shall take the next shot, even if that golfer'sball is closer to the hole than others. This practice can typicallyresult in a more efficient completion of the hole and pace of play,allowing the golfers to complete the hole more quickly and allowinggolfers playing behind to have an opportunity to play the hole sooner.

Accordingly, system 600 is configured to determine which of the golferswill be hitting the next shot, such that cameras 601 may be pointed andfocused on the correct golfer to record the next shot.

For each shot taken by each golfer, cameras 601 record the shot anddetermine, based on the recorded video, the location of each ball withinlocation grid 650. System 600, having identified the golfer for eachshot, thereby will correlate the location of the ball and the golfer whohit the ball. Accordingly, system 600 may determine for each golferwhere the next shot will occur. Similarly, system 600 may determine foreach ball on location grid 650 which golfer will be hitting each ball.Thus, system 600 determines both the location of the ball and theidentity of the golfer.

System 600 may be configured to control which golfer will hit the nextshot, or identify which golfer is about to hit the next shot. In oneaspect, processor 611 may communicate with the golfer to alert thegolfer that it is their turn to hit their next shot. In one aspect, analert or signal may be sent to golfer's mobile device 631. The alert maybe in the form of an audible alert, a visual alert (such as a graphicalrepresentation on the screen of the mobile device), a haptic alert (suchas by activating a vibration function of mobile device 631), or amessage (such as an SMS text message or the like). In this approach, thegolfers may be aware that processor 611 will be instructing which golferis due to take their shot. Processor 611 may send alerts to each of thegolfers at the same time, indicating an order of play, such that thegolfers are alerted as to which golfer will be next to hit after thepresently hitting golfer is finished.

As described above, processor 611 is configured to monitor and detectthe location of each golfer, so by controlling which golfer is due tohit, cameras 601 may therefore be directed toward that golfer to recordthe shot. Cameras 601 may also zoom in on the golfer that is hitting thenext shot.

In another aspect, processor 611 may determine which golfer is about tohit the next shot based on the movement of the golfers relative to theball. For example, if one of the group of the golfers is positionedwithin a predetermined distance, while the rest of the golfers arepositioned further away, processor 611 may determine that the golfernear his ball is the golfer that is about to hit. In response to thisdetermination, processor 611 may instruct the cameras to be pointed atthis golfer, and cameras 601 may zoom in on the golfer.

The relative distance between the golfers and their respective golf balllocations may be determined using grid 650. For example, if a golfer ispositioned within the same grid square or an adjacent grid square to thepreviously determined location of the balls, processor 611 may determinethat this golfer is the one that is about to hit. Processor 611 maycompare the relative distances between the golfers and their respectiveball locations and determine that the golfer closest to his ball is theone that is about to hit. The golfers may be instructed to remain apredetermined distance away from their balls when they are not planningto hit to assist processor 611 in making the determination.

Accordingly, system 600 may be configured to signal to the golfers whichgolfer is to be next to hit, and/or system 600 may be configured todetermine based on the positions of the golfers relative to theirrespective ball locations which golfer has decided to hit next. In bothcases, system 600 may be configured to focus cameras 601 on the correctgolfer such that a recording of the golfer and the upcoming shot isproperly recorded and stored.

This process may be repeated for each successive shot being played onthe hole by the various golfers on the hole. In some instances, the samegolfer may take more than one shot before another golfer takes his nextshot. The process may be repeated until the completion of the hole.

During the play of the hole, cameras 601 may be configured to beconstantly recording, and processor 611 may be configured to tagspecific times of the recording to correspond to the various golfers andshots being taken, such that the recording may be divided and splicedtogether in accordance with the identity of each golfer and each shottaken. Alternatively, the recording may be started and stopped for eachshot that is taken, and each individual recording may be tagged andlater spliced together for each individual golfer.

System 600 may include a local video storage (not expressly shown inFIG. 6 ) such as AI enabled digital video recorder 312 or memory 303illustrated in FIG. 3 , in communication with the processor 611.Alternatively, remote video storage may be used, such as cloud storageand services 404 of FIG. 4 . Processor 611 may communicate with videostorage via Wi-Fi, cellular data, radio communication, or the like.Processor 611 may also communicate via a communication cable to a remoteserver or other communication device that communicates with videostorage.

Processor 611 may further include or be in communication with imageprocessing system/module 112, 300, 118, 400 that is in communicationwith video storage for the recorded video and splice together thevarious recordings of each shot assigned to each golfer. With referenceto FIG. 6 , processor 611 is in communication with local AI videoprocessing system/engine 622 and remote processing system 628. Videoprocessing system/engine 622 may include its own database for storingthe video recordings and formatting the video recordings. The imageprocessing systems/module 622, 628, 112, 300, 118, 400 may be includedwith the processor 611, or may be a separate module, as illustrated inthe various figures. The image processing systems/modules 622, 628, 112,300, 118, 400 may begin processing the images immediately upon thecompletion of each shot, appending the assembled recording with eachadditional shot, or processing may occur after video recording is over.

System 600 may be further configured to determine based on the locationdata of the golfers (via mobile devices 631 or other locatingmechanisms) and geofence 636 boundary when the golfers have left thehole. In response to determining that the golfers have left the hole,processor 611 may provide one or more of the recordings that werespliced or segmented, assembled or combined, and processed and formattedand communicated to the golfers. In one aspect, each golfer may receive,at their mobile device 631 or other device, the recording of theirspecific shots. In another aspect, each golfer may receive all of therecordings of their group and may select which of the recordings toview.

In a preferred aspect, the recordings are provided to the golfers afterthe completion of the hole and after the golfers have left the hole toencourage the golfers to vacate the hole such that trailing golfers mayplay the hole. However, in another approach, the recordings or portionsof the recordings may be provided to the golfers shortly after thecompletion of each shot. In another aspect, the recordings may beprovided to the golfers after they have finished their round and enteredanother location on the golf course, such as the pro shop, restaurant,bar, clubhouse, locker room, or parking lot.

In one aspect, system 600 may be configured to automatically upload theassembled recording of the golfer to the internet in addition toproviding the recording to the individual golfers. Alternatively, therecording may be uploaded to the internet instead of being provideddirectly to the golfer.

In one aspect, the recording may be uploaded to a specific accountassociated with the golfer corresponding to each golfer-specificrecording. For example, the recording may be uploaded automatically toone of the golfer's social media accounts. Each golfer may have a useraccount associated with system 600. For example, via app 630 installedon golfer's mobile device 631, the golfer may enter variousidentification data, such as the golfer's name, address, email address,payment information, photograph, and other identifying characteristicsof the golfer. The golfer may also provide their social media accountsand permission for the app installed on mobile device 631 to postinformation to their linked social media accounts. In one aspect, thegolfer may enter their biographic information and social media accountsinto a database associated with processor 611, rather than entering thisinformation in the app installed on mobile device 631.

In one aspect, the golfer may choose whether to upload the recording totheir social media accounts. The golfer may choose either before orafter the recording whether the recording will be automaticallyuploaded. The golfer may also choose to manually control whether therecording will be uploaded automatically.

The inclusion of multiple cameras 601, in this case cameras 601positioned near both the green and the tee box, allows for each shot tobe recorded from multiple angles. Accordingly, the image processingsoftware may splice together more than one angle of each shot. System600 may determine whether only one angle should be shown, depending onthe distance away from cameras 601 and ability of cameras 601 to zoom inon the particular golfer. In some cases, the golfer may be behind astructure or other obstruction, such that one of the angles ispreferable to another. In some cases, the golfer may be too far awayfrom one of cameras 601 for a desirable recording. For example, when thegolfer is putting or near the putting green, camera 601 near the tee boxmay not provide a desirable angle. However, when the golfer is hittingfrom the tee box, while the golfer may be far away, the ball flight mayresult in the ball landing near the camera 601 at the green, andtherefore the angles from both the tee box and the green may be used. Inanother case, the golfer may be generally midway between the tee andgreen, and therefore the angles from both cameras 601 may be desirable.

The above-described aspects provide for the ability to perform“un-manned” or autonomous videography of golfers, similar to the videorecordings of golfers competing on television.

System 600 may be further configured to add graphical elements to therecordings of the golf shots for entertainment and evaluation purposes.

In one aspect, system 600 may be configured to analyze the imagescaptured by cameras 601 and processed by video processingsystems/modules/engines 622, 628 (or 112, 118, 300, 400) to provideadditional image enhancements to the recorded video. For example, system600 may include “tracer” software that will provide a visual indicationof the flight of the ball. For example, in many television broadcasts ofprofessional golfing events, when a golfer strikes a ball, a coloredline trails the ball and leaves a colored path of the ball flight on theimage being broadcast. The curvature of the path is provided on thebroadcast image showing how the ball may have travelled, hooked, sliced,faded, or the like. The tracer software may determine the speed of theball, the distance the ball travelled, the apex or height of the ballduring the shot, or other aspects.

These images with the tracers included in the video or recording providea more robust accounting of the flight path of the ball than istypically possible for the typical viewer, especially once the ball hastraveled a relatively far distance from the camera. In broadcastswithout the tracer, it is sometimes difficult to pick up the path of theball in the latter portion of the ball flight, and the broadcast willoften switch to a different view, showing the landing area of the ball,leaving the viewer with incomplete information of how the ball traveled.Video processing systems 622, 628 (or 112, 118, 300, 400) associatedwith system 600 may therefore provide the user with more completeinformation related to their golf shot relative to images without thetracer.

Other graphical representations may be added to the flight of the ballin addition to tracing the path of the ball. For example, depending onthe speed, distance, or ball flight, the flight path may be color-codedto indicate a particular achievement. For example, if the ball travelsabove a certain speed, a red or “hot” color may be applied to indicate ahigh speed, or a flame-graphic may be added as a tail to the ball.Similarly, if the ball flight is within a range of being considered“straight,” a green color may be applied to the ball flight to indicatethe lack of a hook or slice. Conversely, if the ball flight is notstraight, another color, such as yellow or red, may be applied to theball flight to indicate a less than ideal shot. It will be appreciatedthat these graphical additions based on the ball flight may be tailoredto provide various colors or graphical representations. In one aspect,the golfer may indicate, via the mobile device 631, the type ofindicator they would like to have displayed. The type of indicator maybe toggled, such that multiple types of indicators may be applied to thesame recording.

Graphical elements may also be added to the recordings based on thelocation of where the ball landed at the conclusion of each shot. Asdescribed above, system 600 may include location grid 650 that isassociated with the topography of the hole. For example, each gridsquare of location grid 650 may be correlated with a topographicalaspect of the hole. Select squares may be associated with a bunker,water hazard, out of bounds, in the woods, on the fairway, in the rough,on the green, etc. System 600, cameras 601, and processor 611 maydetermine based on the recording and the flight of the ball where theball ended up within grid 650 and, accordingly, the type of locationwhere the ball ended up. Based on the location of the ball, system 600may add a graphical element.

For example, if the ball is determined to have landed in a water hazard,a graphical element, such as a sea monster, may be added to therecording at the location of the ball. Similarly, a splash illustrationmay be added, or a snorkeler, or the like. If the ball is determined tohave landed in a bunker or sand trap, a beach ball or beach umbrella maybe added to the recording in the location of the ball. If the ball isdetermined to have landed in the rough, a lawnmower illustration may beadded to the location of the ball. If the ball is determined to havelanded in the woods, a squirrel or bear may be added that approaches theball and appears to run away with it. If the ball lands on the green,sometimes referred to as “the dancefloor,” an illustration of a danceror disco ball may be added. It will be appreciated that various othertypes of ball locations and corresponding animations or illustrationsmay be applied based on the location. System 600 may include multipleanimations for the same type of location, such that each instance may bea relatively unique animation. The animations may be randomly assignedbased on the type of location, or they may be cycled for identifiedgolfers such that repeat animations are limited.

Location grid 650 may also be used for additional purposes. In oneaspect, location grid 650 may be used to help a golfer locate theirball. In many cases, a golfer may have trouble seeing the flight oftheir ball after a shot, and may not know where ball is located.Processor 611 may provide the location data of the ball to the golfersuch that the golfer does not have to spend additional time searchingfor the ball's landing spot. Thus, the golfers may be able to completethe hole in a more efficient manner, improving the pace of play.

Similarly, based on the location of the ball within the grid and thelocation of other features of the hole that are correlated with locationgrid 650, system 600 may provide additional information to the golferregarding his upcoming shot. For example, processor 611 may communicateto the golfer the distance to the pin at the conclusion of the shot,allowing the golfer to consider which club to use for their next shotprior to arriving at the ball's location. System 600 may provide otherdistance based information, such as the distance to other features ofthe hole, such as the distance necessary to clear a water hazard orbunker, or the distance to a particular area of the green.

As described above, location grid 650 may be overlaid on the hole, whichincludes green 604. Accordingly, system 600 may also be configured tooperate as a virtual caddie to assist the golfers with putts. System 600may include various information about the green that is stored in memory303, cloud storage and service 404, or other database storage incommunication with system 600. For example, the various undulations ofthe surface of the green may be stored, which may be referenced todetermine the expected break on a particular putt. For example,processor 611 may communicate to the golfer that an upcoming putt willbreak 3 inches to the left. Processor 611 may indicate whether the puttis uphill or downhill. Additionally, processor 611 may store variousrecordings of putts made from various locations on the green andaggregate these putts to use artificial intelligence to determine howputts travel from certain locations on the green. For example, thetemperature, wind, moisture, and grain direction can affect how a putttravels that is not readily apparent from the shape of the green, andcan change over time and weather conditions. By analyzing the results ofrecorded putts from different locations, processor 611 may utilize thisdata and update its recommendation for different locations. In oneaspect, location grid 650 associated with the green may have smallergrid squares to provide more accurate location data to provide thevirtual caddie assistance.

System 600 may further be used to automatically identify the brandsbeing used by the golfers. Similar to the facial recognition describedabove, processor 611, via cameras 601, may identify the brand of golfclub the golfer is using, or the brand of clothing the golfer iswearing. Processor 611 may utilize the image recognition ability toidentify logos, patterns, trademarks, or the like associated withdifferent brands. In response to identifying the brands associated witheach golfer, processor 611 may be configured to communicate with thegolfer to provide information regarding the brands, such as newproducts, product offers, or alternative products.

System 600 may also include the ability to integrate various softwareapplications that may be used by the golfer or the golf course owner.Mobile device 631 may include user app 630 that may be operated toindicate a location of the user relative to the golf hole, which may beperformed manually or automatically. As described above, system 600 mayautomatically determine that the golfer has arrived at a particular golfhole. User app 630 may also be used to manually signal to processor 611that the golfer has arrived at the hole. Similarly, user app 630 mayprovide a notification to the golfer that they have arrived at a holewhere recording is available. User app 630 may provide otherfunctionality, such as allowing the golfer to decline that their shotwill be recorded.

The proprietor of the golf course where system 600 is used may also havea dedicated application that interfaces with processor 611. Thisapplication may be referred to as a course app 632. Course app 632 maybe configured to communicate with user app 630. Course app 632 may allowfor the golf course to register the golf course as a course thatincludes the recording capability. User app 630 may receive informationfrom various golf courses that register, providing end users withinformation about which courses are available that provide the recordingcapability.

Course app 632 may communicate with user app 630 to provide anotification to the course that an interested user is on-site. An alertmay be provided via course app 632 that a user or group of users hasarrived, and that the users are interested in using the technology.Similarly, user app 630 may provide an alert to a golfer that a nearbycourse or the course at which the golfers are preparing to play includesthe recording capability.

User app 630 may also provide the ability to request recording in themiddle of a round. In another approach, course app 632 may be used bythe golf course during golfer check-in to provide the service forinterested golfers. However, it can be the case that a golfer may changehis mind regarding whether or not to record a shot. Accordingly,providing the ability in user app 630 to request recording or declinepreviously requested recording may ensure that user needs are met.

User app 630 may also provide a mechanism for paying for particularfeatures of a shot either before or after the shot is recorded. Forexample, in the event a shot is recorded, the recording and imageprocessing has the ability to provide the above-described tracertechnology to the shot to show the path of the ball. This may beconsidered an added feature, and the user may choose after hitting theshot whether or not to apply the tracer technology. However, in anotheraspect, the tracer technology may be applied regardless of user input.User app 630 may provide the ability to have the tracer turned on or offon demand.

User app 630 may communicate with other user apps 630 for other golfers.For example, each of the members of a particular group of golfers mayhave their user app 630 active, with each of the golfers having theirshot recorded. The shots of each of the golfers may be aggregated by oneor more of user apps 630, providing a composite of the shots for eachgolfer. The shots may be overlaid on each other or displayed one afterthe other. User app 630 may provide other functionality among the groupof golfers, such as the location of each golfer on the course, eachgolfer's distance to the hole, etc., which may provide for desirablebenefits among the group for competitive or entertainment purposes.

According to an aspect, user app 630 may provide furthercamera/recording control by the golfer to tailor the recording asdesired. In one aspect, the golfer may use user app 630 to activatevideo recording using a record button displayed via app 630 or otherwiseprovided on mobile device 631, which enables cameras 601 to record thegolfer. In a related aspect, a stop button may be similarly providedthat disables cameras 601, for example while the golfer is reading thegreen.

In another aspect, recording of the golfer may be automatically stoppedaccording to predetermined programming. In one aspect, geofence 636and/or position sensing technology may be used in combination with theabove-described manual activation to turn off cameras 601 if the userforgets to stop the recording.

Note that not all of the activities described above in the generaldescription or the examples are required, that a portion of a specificactivity may not be required, and that one or more further activitiesmay be performed in addition to those described. Still further, theorders in which activities are listed are not necessarily the order inwhich they are performed.

The specification and illustrations of the embodiments described hereinare intended to provide a general understanding of the structure of thevarious embodiments. The specification and illustrations are notintended to serve as an exhaustive and comprehensive description of allof the elements and features of apparatus and systems that use thestructures or methods described herein. Many other embodiments may beapparent to those of skill in the art upon reviewing the disclosure.Other embodiments may be used and derived from the disclosure, such thata structural substitution, logical substitution, or another change maybe made without departing from the scope of the disclosure. Accordingly,the disclosure is to be regarded as illustrative rather thanrestrictive.

Certain features are, for clarity, described herein in the context ofseparate embodiments, may also be provided in combination in a singleembodiment. Conversely, various features that are, for brevity,described in the context of a single embodiment, may also be providedseparately or in any sub combination. Further, reference to valuesstated in ranges includes each and every value within that range.

Benefits, other advantages, and solutions to problems have beendescribed above with regard to specific embodiments. However, thebenefits, advantages, solutions to problems, and any feature(s) that maycause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as a critical, required, or essentialfeature of any or all the claims.

The above-disclosed subject matter is to be considered illustrative, andnot restrictive, and the appended claims are intended to cover any andall such modifications, enhancements, and other embodiments that fallwithin the scope of the present invention. Thus, to the maximum extentallowed by law, the scope of the present invention is to be determinedby the broadest permissible interpretation of the following claims andtheir equivalents, and shall not be restricted or limited by theforegoing detailed description.

The above-described system 600 may be embodied as a group of associatedcomponents that are controllable by processor 611 and softwareassociated therewith. System 600 and various aspects of its use may alsobe embodied as methods that utilizes the above-described functionalityto automatically provide the end user with the benefits described above.System 600 may include various associated software modules that may beimplemented using processor 611 or remotely and in communication withprocessor 611. The modules or methods may include various artificialintelligence and machine learning and image processing to automaticallyprocess the various images and provide the desired output to the enduser. FIGS. 7 and 8 below illustrate examples of providing a method thatcan be used to automatically record and process video for a golfactivity in accordance with aspects of the disclosure.

FIG. 7 illustrates one aspect of a method 700 associated with the abovedescribed systems. In one aspect, the method 700 of automaticallyrecording an athletic performance is provided. The method 700 includes:at step 702, detecting, by a processor, that at least one player ispositioned within a predetermined area; at step 704 identifying a firstplayer of the at least one player; at step 706, automatically recording,by at least one camera operatively coupled to the processor, aperformance of the first player and defining a first recording; at step708, automatically storing, in a database operatively coupled to theprocessor, the first recording; at step 710 automatically correlatingthe first recording with the first player; and, at step 712,automatically processing the first recording and defining a firstprocessed recording.

FIG. 8 illustrates one aspect of a method 800 of automatically recordingand providing video in accordance with aspects, of the above-describedsystems. The method 800 may include: at step 802, recording a video of apredetermined activity using a first remote camera located at a firstgeographic location; and at step 804, processing the video at the firstgeographic location. The processing may include: at step 806,identifying a first user performing the predetermined activity; at step808, extracting image frames from the video including the first userduring the predetermined activity; and at step 810, merging theextracted image frames to generate a formatted video. The method 800 mayalso include, at step 812, outputting the formatted video to a remotevideo processing system for additional processing.

It will be appreciated that various other additional method steps maybeincluded in the above method, or the above method may be modified inaccordance with the functionality of the system 100 described above.

It will be appreciated that such aspects and embodiments are more thanan abstract idea performed by a computer or other controller. Theabove-described aspects are automatically performed based on a varietyof inputs that are not easily accessible or determined, and theresulting end product cannot otherwise be provided in the same automaticmanner.

Although only a few exemplary embodiments have been described in detailabove, those skilled in the art will readily appreciate that manymodifications are possible in the exemplary embodiments withoutmaterially departing from the novel teachings and advantages of theembodiments of the present disclosure. Accordingly, all suchmodifications are intended to be included within the scope of theembodiments of the present disclosure as defined in the followingclaims. In the claims, means-plus-function clauses are intended to coverthe structures described herein as performing the recited function andnot only structural equivalents, but also equivalent structures.

What is claimed is:
 1. A system for automatically recording andproviding video comprising: at least one fixed AI enabled camera locatedat a ski resort and configured to activate a video recording and recorda video of at least one of a skier and a snowboarder at the ski resort,the fixed AI enabled camera positioned to at least a portion of a pathof the at least one of the skier and the snowboarder; a processingresource operably coupled to the at least one fixed AI enabled camera;at least one remote identifier carried by the at least one of the skierand the snowboarder and in communication with the processing resourceand configured to transmit location data of the at least one of theskier and the snowboarder thereto; the processing resource configured toreceive the location data from the at least one remote identifier andprocess the video, wherein the video includes at least one of a skiingactivity and a snowboarding activity that occurred at the ski resort;and an AI logic accessible to the at least one fixed AI enabled cameraand the processing resource, the AI logic configured to identify the atleast one of the skier and the snowboarder within at least a portion ofthe video and extract segments of the video associated with the at leastone of the skier and the snowboarder and combine the segments into acomposite video for the at least one of the skier and the snowboarder.2. The system of claim 1, wherein the at least one remote identifierincludes an RFID device associated with the at least one of the skierand the snowboarder capable of transmitting radio frequencies to locatethe at least one of the skier and the snowboarder at the ski resort. 3.The system of claim 1, wherein the at least one remote identifierincludes a mobile device of the at least one of the skier and thesnowboarder capable of communicating the location data using locationservices of the mobile device to locate the at least one of the skierand the snowboarder at the ski resort.
 4. The system of claim 1, whereinthe AI logic is further configured to identify a clothes color worn bythe at least one of the skier and the snowboarder.
 5. The system ofclaim 1, further including at least one sensor in communication with theprocessing resource and configured to sense activity at the ski resortand send signals thereto and the processing resource is configured toreceive the signals from the at least one sensor and process the videobased on the signals from the at least one sensor.
 6. The system ofclaim 5, wherein the at least one sensor includes a Doppler Radar Sensorinstalled at the ski resort, the Doppler Radar Sensor configured tocapture Doppler Radar Sensor data during the video recording, theDoppler Radar Sensor further configured to communicate the Doppler RadarSensor data to the processing resource.
 7. The system of claim 6,wherein the processing resource is configured to use the Doppler RadarSensor data and at least one video frame of the video to process thevideo to produce at least a portion of a graphic added to the video. 8.The system of claim 7, wherein the graphic includes a colored traceindicating the path of the at least one of the skier and thesnowboarder.
 9. The system of claim 6, wherein at least one video frameof the video includes a first video frame and a second video frame andthe processing resource is configured to: process the first video frameusing the Doppler Radar Sensor data and identify a first location of theat least one of the skier and the snowboarder within the first videoframe; process the second video frame using the Doppler Radar Sensordata and identify the at least one of the skier and the snowboarderlocated at a second location within the second video frame; create atrace between the first location and the second location to showmovement of the at least one of the skier and the snowboarder betweenthe first video frame and the second video frame; and continueprocessing the video to add the trace until the at least one of theskier and the snowboarder either comes to a rest or is no longer visiblewithin the at least one video frame.
 10. The system of claim 1, whereinthe processing resource is configured to: identify a first location ofthe at least one of the skier and the snowboarder within a first videoframe of the video using the AI logic; process a second video frame ofthe video using the AI logic to identify the at least one of the skierand the snowboarder located at a different location than the firstlocation; create a trace between the first location of the first videoframe and the different location of the second video frame to showmovement of the at least one of the skier and the snowboarder betweenthe first video frame and the second video frame; and continueprocessing the video to extend the trace until the at least one of theskier and the snowboarder either comes to a rest or is no longer visiblewithin a video frame of the video.
 11. A method for automaticallyproviding a video, the method comprising: detecting a presence of atleast one of a skier and a snowboarder at a ski resort; activating arecording of the at least one of the skier and the snowboarder using anAI enabled camera located at the ski resort, wherein the AI enabledcamera includes access to an AI logic used to identify the at least oneof the skier and the snowboarder; identifying the at least one of theskier and the snowboarder using the AI logic; recording at least one ofa skiing activity and a snowboarding activity at the ski resort usingthe AI enabled camera, the at least one of the skiing activity and thesnowboarding activity including a video segment of a path of the atleast one of the skier and the snowboarder including at least two videoframes, wherein the at least one of the skier and the snowboarder islocated in different locations within the at least two video frames; andprocessing the video segment to include a visual indication of at leasta portion of the path of the at least one of the skier and thesnowboarder based on the different locations within the at least twovideo frames.
 12. The method of claim 11, wherein the AI logic includesa neural network of tagged objects including one or more of: the atleast one of the skier and the snowboarder; a shirt; a shirt color;pants; a pants color; a hat; a hat color; a male; a female; a child; anda sunglasses.
 13. The method of claim 11, further comprising:associating the video with the at least one of the skier and thesnowboarder identified; and enabling access to the video to the at leastone of the skier and the snowboarder identified.
 14. The method of claim11, further including using the AI logic to: identify a clothes colorworn by the at least one of the skier and the snowboarder; identify whenthe at least one of the skiing activity and the snowboarding activity ofthe at least one of the skier and the snowboarder begins; and identifywhen the at least one of the skiing activity and the snowboardingactivity of the at least one of the skier and the snowboarder ends. 15.The method of claim 11 further comprising: activating a Doppler Sensoracross a portion of the ski resort; recording Doppler Sensor data duringthe recording of the at least one of the skiing and the snowboardingactivity; and using at least a portion of the Doppler Sensor data duringthe processing the video segment.
 16. The method of claim 11, whereinthe visual indication of the at least the portion of the path of the atleast one of the skier and the snowboarder is a colored trace.
 17. Themethod of claim 11, further including: transmitting radio frequencies tolocate the at least one of the skier and the snowboarder at the skiresort using an RFID device associated with the at least one of theskier and the snowboarder; and identifying the at least one of the skierand the snowboarder using both the AI logic and the radio frequenciesfrom the RFID device locating the at least one of the skier and thesnowboarder at the ski resort.
 18. The method of claim 11, furtherincluding: communicating location data using location services of amobile device of the at least one of the skier and the snowboarder tolocate the at least one of the skier and the snowboarder at the skiresort; and identifying the at least one of the skier and thesnowboarder using both the AI logic and the location data from themobile device locating the at least one of the skier and the snowboarderat the ski resort.
 19. The method of claim 11, further including:extracting segments of the video associated with the at least one of theskier and the snowboarder using the AI logic; and combining the segmentsinto a composite video for the at least one of the skier and thesnowboarder using the AI logic.
 20. An on-site ski and snowboardingrecording system comprising: a fixed AI enabled camera located at a skiresort, the fixed AI enabled camera configured to activate a videorecording of at least one of a skier and a snowboarder at the skiresort, the fixed AI enabled camera positioned to view at least aportion of a path of the at least one of the skier and the snowboarder;a processing resource configured to process the video locally at the skiresort, wherein the video includes at least one of a skiing activity anda snowboarding activity that occurred at the ski resort; an RFID deviceassociated with the at least one of the skier and the snowboarder incommunication with the processing resource and capable of transmittingradio frequencies to locate the at least one of the skier and thesnowboarder at the ski resort; and an AI logic accessible to the fixedAI enabled camera and the processing resource, the AI logic configuredto identify the at least one of the skier and the snowboarder within atleast a portion of the video and extract segments of the videoassociated with the at least one of the skier and the snowboarder andcombine the segments into a composite video for the at least one of theskier and the snowboarder, wherein at least a portion of the AI logic isstored within the fixed AI enabled camera thereby facilitating videoprocessing locally.
 21. The system of claim 20, further including amobile device of the at least one of the skier and the snowboardercapable of communicating location data of the at least one of the skierand the snowboarder using location services of the mobile device tolocate the at least one of the skier and the snowboarder at the skiresort.
 22. The system of claim 20, wherein the AI logic is furtherconfigured to identify a clothes color worn by the at least one of theskier and the snowboarder.
 23. The system of claim 20, further includinga cellular modem coupled to the processing resource and configured tocommunicate with a wireless network capable of accessing Cloud basedservices as at least a portion of the processing resource.
 24. Thesystem of claim 23, wherein the cellular modem is further configured tocommunicate the video after processing to the at least one of the skierand the snowboarder.
 25. The system of claim 20, wherein the fixed AIenabled camera includes at least a portion of the AI logic accessible tothe processing resource to process the video to identify the at leastone of the skier and the snowboarder at the ski resort.